human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "aff wild2 extending the aff wild database for affect recognition"
  Abstract: Automatic understanding of human affect using visual signals is a problem that has attracted significant interest over the past 20 years. However, human emotional states are quite complex. To appraise such states displayed in real-world settings, we need expressive emotional descriptors that are capable of capturing and describing this complexity. The circumplex model of affect, which is described in terms of valence (i.e., how positive or negative is an emotion) and arousal (i.e., power of the activation of the emotion), can be used for this purpose. Recent progress in the emotion recognition domain has been achieved through the development of deep neural architectures and the availability of very large training databases. To this end, Aff-Wild has been the first large-scale "in-the-wild" database, containing around 1,200,000 frames. In this paper, we build upon this database, extending it with 260 more subjects and 1,413,000 new video frames. We call the union of Aff-Wild with the additional data, Aff-Wild2. The videos are downloaded from Youtube and have large variations in pose, age, illumination conditions, ethnicity and profession. Both database-specific as well as cross-database experiments are performed in this paper, by utilizing the Aff-Wild2, along with the RECOLA database. The developed deep neural architectures are based on the joint training of state-of-the-art convolutional and recurrent neural networks with attention mechanism; thus exploiting both the invariant properties of convolutional features, while modeling temporal dynamics that arise in human behaviour via the recurrent layers. The obtained results show premise for utilization of the extended Aff-Wild, as well as of the developed deep neural architectures for visual analysis of human behaviour in terms of continuous emotion dimensions.

Connected through hyperedge HE-1 (induced by source paper "lossless attention in convolutional networks for facial expression recognition in the wild"):
  with members:
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "eca net efficient channel attention for deep convolutional neural networks"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "region attention networks for pose and occlusion robust facial expression recognition"
    - "frame attention networks for facial expression recognition in videos"
    - "local learning with deep and handcrafted features for facial expression recognition"
    - "covariance pooling for facial expression recognition"

Connected through hyperedge HE-2 (induced by source paper "analysing affective behavior in the first abaw 2020 competition"):
  with members:
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "avec 2019 workshop and challenge state of mind detecting depression with ai and cross cultural affect recognition"
    - "exploiting multi cnn features in cnn rnn based dimensional emotion recognition on the omg in the wild dataset"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "facial expression recognition using disentangled adversarial learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a multi component cnn rnn approach for dimensional emotion recognition in the wild"
    - "efficient augmentation via data subsampling"

Connected through hyperedge HE-3 (induced by source paper "two stream aural visual affect analysis in the wild"):
  with members:
    - "multi label class balancing algorithm for action unit detection"
    - "analysing affective behavior in the first abaw 2020 competition"
    - "fau facial expressions valence and arousal a multi task solution"
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "context aware emotion recognition networks"
    - "retinaface single stage dense face localisation in the wild"
    - "face behavior a la carte expressions affect and action units in a single network"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-4 (induced by source paper "m 3 t multi modal continuous valence arousal estimation in the wild"):
  with members:
    - "facial affect recognition in the wild using multi task learning convolutional network"
    - "analysing affective behavior in the first abaw 2020 competition"
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "retinaface single stage dense face localisation in the wild"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "pytorch an imperative style high performance deep learning library"
    - "large scale visual speech recognition"

Connected through hyperedge HE-5 (induced by source paper "adversarial based neural networks for affect estimations in the wild"):
  with members:
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "avec 2019 workshop and challenge state of mind detecting depression with ai and cross cultural affect recognition"
    - "end to end facial and physiological model for affective computing and applications"
    - "sewa db a rich database for audio visual emotion and sentiment research in the wild"
    - "deep autoencoder for combined human pose estimation and body model upscaling"
    - "a multi task learning generation framework valence arousal action units primary expressions"
    - "adversarial training in affective computing and sentiment analysis recent advances and perspectives"
    - "semi supervised learning with gans manifold invariance with improved inference"

Connected through hyperedge HE-6 (induced by source paper "emotion recognition for in the wild videos"):
  with members:
    - "analysing affective behavior in the first abaw 2020 competition"
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "retinaface single stage dense face localisation in the wild"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "pytorch an imperative style high performance deep learning library"
    - "a multi task learning generation framework valence arousal action units primary expressions"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-7 (induced by source paper "multitask emotion recognition with incomplete labels"):
  with members:
    - "analysing affective behavior in the first abaw 2020 competition"
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "face behavior a la carte expressions affect and action units in a single network"
    - "mimamo net integrating micro and macro motion for video emotion recognition"
    - "model distillation with knowledge transfer from face classification to alignment and verification"
    - "the omg emotion behavior dataset"
    - "emotionet challenge recognition of facial expressions of emotion in the wild"
    - "from facial expression recognition to interpersonal relation prediction"

Connected through hyperedge HE-8 (induced by source paper "affective expression analysis in the wild using multi task temporal statistical deep learning model"):
  with members:
    - "analysing affective behavior in the first abaw 2020 competition"
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "a multi task learning generation framework valence arousal action units primary expressions"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "deep residual learning for image recognition"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.CV(Computer Vision and Pattern Recognition)