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: "a multi task learning generation framework valence arousal action units primary expressions"
  Abstract: Over the past few years many research efforts have been devoted to the field of affect analysis. Various approaches have been proposed for: i) discrete emotion recognition in terms of the primary facial expressions; ii) emotion analysis in terms of facial Action Units (AUs), assuming a fixed expression intensity; iii) dimensional emotion analysis, in terms of valence and arousal (VA). These approaches can only be effective, if they are developed using large, appropriately annotated databases, showing behaviors of people in-the-wild, i.e., in uncontrolled environments. Aff-Wild has been the first, large-scale, in-the-wild database (including around 1,200,000 frames of 300 videos), annotated in terms of VA. In the vast majority of existing emotion databases, their annotation is limited to either primary expressions, or valence-arousal, or action units. In this paper, we first annotate a part (around $234,000$ frames) of the Aff-Wild database in terms of $8$ AUs and another part (around $288,000$ frames) in terms of the $7$ basic emotion categories, so that parts of this database are annotated in terms of VA, as well as AUs, or primary expressions. Then, we set up and tackle multi-task learning for emotion recognition, as well as for facial image generation. Multi-task learning is performed using: i) a deep neural network with shared hidden layers, which learns emotional attributes by exploiting their inter-dependencies; ii) a discriminator of a generative adversarial network (GAN). On the other hand, image generation is implemented through the generator of the GAN. For these two tasks, we carefully design loss functions that fit the examined set-up. Experiments are presented which illustrate the good performance of the proposed approach when applied to the new annotated parts of the Aff-Wild database.

Connected through hyperedge HE-1 (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-2 (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-3 (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-4 (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"
    - "aff wild2 extending the aff wild database for affect recognition"
    - "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-5 (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"
    - "aff wild2 extending the aff wild database for affect recognition"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-6 (induced by source paper "unique class group based multi label balancing optimizer for action unit detection"):
  with members:
    - "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"
    - "towards real time head pose estimation exploring parameter reduced residual networks on in the wild datasets"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep residual learning for image recognition"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-7 (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"
    - "aff wild2 extending the aff wild database for affect recognition"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "multi label class balancing algorithm for action unit detection"):
  with members:
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "towards real time head pose estimation exploring parameter reduced residual networks on in the wild datasets"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "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)