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 component cnn rnn approach for dimensional emotion recognition in the wild"
  Abstract: This paper presents our approach to the One-Minute Gradual-Emotion Recognition (OMG-Emotion) Challenge, focusing on dimensional emotion recognition through visual analysis of the provided emotion videos. The approach is based on a Convolutional and Recurrent (CNN-RNN) deep neural architecture we have developed for the relevant large AffWild Emotion Database. We extended and adapted this architecture, by letting a combination of multiple features generated in the CNN component be explored by RNN subnets. Our target has been to obtain best performance on the OMG-Emotion visual validation data set, while learning the respective visual training data set. Extended experimentation has led to best architectures for the estimation of the values of the valence and arousal emotion dimensions over these data sets.

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"
    - "efficient augmentation via data subsampling"
    - "a multi task learning generation framework valence arousal action units primary expressions"

Connected through hyperedge HE-2 (induced by source paper "fau facial expressions valence and arousal a multi task solution"):
  with members:
    - "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"
    - "from facial expression recognition to interpersonal relation prediction"

Connected through hyperedge HE-3 (induced by source paper "emotion generation and recognition a stargan approach"):
  with members:
    - "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"
    - "training deep neural networks with different datasets in the wild the emotion recognition paradigm"
    - "generating faces for affect analysis"
    - "a multi task learning generation framework valence arousal action units primary expressions"
    - "aff wild2 extending the aff wild database for affect recognition"
    - "photorealistic facial synthesis in the dimensional affect space"
    - "learning to discover cross domain relations with generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "exploiting multi cnn features in cnn rnn based dimensional emotion recognition on the omg in the wild dataset"):
  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"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "training deep neural networks with different datasets in the wild the emotion recognition paradigm"
    - "the omg emotion behavior dataset"
    - "a deep network for arousal valence emotion prediction with acoustic visual cues"
    - "audeering s approach to the one minute gradual emotion challenge"
    - "a multi task learning generation framework valence arousal action units primary expressions"

Connected through hyperedge HE-5 (induced by source paper "ai in pursuit of happiness finding only sadness multi modal facial emotion recognition challenge"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "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"
    - "training deep neural networks with different datasets in the wild the emotion recognition paradigm"
    - "generating faces for affect analysis"
    - "an occam s razor view on learning audiovisual emotion recognition with small training sets"
    - "deep facial expression recognition a survey"
    - "photorealistic facial synthesis in the dimensional affect space"

Connected through hyperedge HE-6 (induced by source paper "aff wild database and affwildnet"):
  with members:
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "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"
    - "training deep neural networks with different datasets in the wild the emotion recognition paradigm"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "generating faces for affect analysis"
    - "a multi task learning generation framework valence arousal action units primary expressions"
    - "aff wild2 extending the aff wild database for affect recognition"

Connected through hyperedge HE-7 (induced by source paper "interpretable deep neural networks for facial expression and dimensional emotion recognition in the wild"):
  with members:
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "exploiting multi cnn features in cnn rnn based dimensional emotion recognition on the omg in the wild dataset"
    - "training deep neural networks with different datasets in the wild the emotion recognition paradigm"
    - "transfer learning for action unit recognition"
    - "a multi task learning generation framework valence arousal action units primary expressions"
    - "aff wild2 extending the aff wild database for affect recognition"
    - "joint unsupervised learning of deep representations and image clusters"
    - "going deeper with convolutions"

Connected through hyperedge HE-8 (induced by source paper "mimamo net integrating micro and macro motion for video emotion recognition"):
  with members:
    - "the omg emotion behavior dataset"
    - "emotion recognition in speech using cross modal transfer in the wild"
    - "a deep network for arousal valence emotion prediction with acoustic visual cues"
    - "multimodal emotion recognition for one minute gradual emotion challenge"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"

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)