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: "adversarial training in affective computing and sentiment analysis recent advances and perspectives"
  Abstract: Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains. As a potentially crucial technique for the development of the next generation of emotional AI systems, we herein provide a comprehensive overview of the application of adversarial training to affective computing and sentiment analysis. Various representative adversarial training algorithms are explained and discussed accordingly, aimed at tackling diverse challenges associated with emotional AI systems. Further, we highlight a range of potential future research directions. We expect that this overview will help facilitate the development of adversarial training for affective computing and sentiment analysis in both the academic and industrial communities.

Connected through hyperedge HE-1 (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"
    - "aff wild2 extending the aff wild database for affect recognition"
    - "semi supervised learning with gans manifold invariance with improved inference"

Connected through hyperedge HE-2 (induced by source paper "snore gans improving automatic snore sound classification with synthesized data"):
  with members:
    - "generative adversarial networks an overview"
    - "data augmentation in emotion classification using generative adversarial networks"
    - "learning to fuse music genres with generative adversarial dual learning"
    - "learning audio sequence representations for acoustic event classification"
    - "segan speech enhancement generative adversarial network"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "adversarial semi supervised audio source separation applied to singing voice extraction"

Connected through hyperedge HE-3 (induced by source paper "data augmentation for low resource sentiment analysis using generative adversarial networks"):
  with members:
    - "on enhancing speech emotion recognition using generative adversarial networks"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "towards open set identity preserving face synthesis"
    - "generative adversarial talking head bringing portraits to life with a weakly supervised neural network"
    - "semi supervised and transfer learning approaches for low resource sentiment classification"
    - "exprgan facial expression editing with controllable expression intensity"
    - "semi supervised learning with context conditional generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "emobed strengthening monomodal emotion recognition via training with crossmodal emotion embeddings"):
  with members:
    - "dynamic difficulty awareness training for continuous emotion prediction"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "the omg emotion behavior dataset"
    - "emotion recognition in speech using cross modal transfer in the wild"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "avec 2016 depression mood and emotion recognition workshop and challenge"
    - "soundnet learning sound representations from unlabeled video"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

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.CL(Computation and Language)