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: "avec 2019 workshop and challenge state of mind detecting depression with ai and cross cultural affect recognition"
  Abstract: The Audio/Visual Emotion Challenge and Workshop (AVEC 2019) "State-of-Mind, Detecting Depression with AI, and Cross-cultural Affect Recognition" is the ninth competition event aimed at the comparison of multimedia processing and machine learning methods for automatic audiovisual health and emotion analysis, with all participants competing strictly under the same conditions. The goal of the Challenge is to provide a common benchmark test set for multimodal information processing and to bring together the health and emotion recognition communities, as well as the audiovisual processing communities, to compare the relative merits of various approaches to health and emotion recognition from real-life data. This paper presents the major novelties introduced this year, the challenge guidelines, the data used, and the performance of the baseline systems on the three proposed tasks: state-of-mind recognition, depression assessment with AI, and cross-cultural affect sensing, respectively.

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"
    - "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"
    - "a multi task learning generation framework valence arousal action units primary expressions"

Connected through hyperedge HE-2 (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"
    - "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"
    - "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-3 (induced by source paper "emopain challenge 2020 multimodal pain evaluation from facial and bodily expressions"):
  with members:
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "recurrent convolutional neural network regression for continuous pain intensity estimation in video"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "detecting depression in dyadic conversations with multimodal narratives and visualizations"):
  with members:
    - "allennlp a deep semantic natural language processing platform"
    - "dense captioning events in videos"
    - "vqa visual question answering"

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.HC(Human-Computer Interaction)