human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "adversarial based neural networks for affect estimations in the wild"):
  Abstract: There is a growing interest in affective computing research nowadays given its crucial role in bridging humans with computers. This progress has been recently accelerated due to the emergence of bigger data. One recent advance in this field is the use of adversarial learning to improve model learning through augmented samples. However, the use of latent features, which is feasible through adversarial learning, is not largely explored, yet. This technique may also improve the performance of affective models, as analogously demonstrated in related fields, such as computer vision. To expand this analysis, in this work, we explore the use of latent features through our proposed adversarial-based networks for valence and arousal recognition in the wild. Specifically, our models operate by aggregating several modalities to our discriminator, which is further conditioned to the extracted latent features by the generator. Our experiments on the recently released SEWA dataset suggest the progressive improvements of our results. Finally, we show our competitive results on the Affective Behavior Analysis in-the-Wild (ABAW) challenge dataset

Includes members:
  Member "expression affect action unit recognition aff wild2 multi task learning and arcface":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lossless attention in convolutional networks for facial expression recognition in the wild")
  Member "avec 2019 workshop and challenge state of mind detecting depression with ai and cross cultural affect recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "analysing affective behavior in the first abaw 2020 competition")
  Member "end to end facial and physiological model for affective computing and applications":
    also connected through other hyperedges:
      (none)
  Member "sewa db a rich database for audio visual emotion and sentiment research in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "modeling emotion in complex stories the stanford emotional narratives dataset")
  Member "deep autoencoder for combined human pose estimation and body model upscaling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "weakly supervised discovery of geometry aware representation for 3d human pose estimation")
  Member "a multi task learning generation framework valence arousal action units primary expressions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "analysing affective behavior in the first abaw 2020 competition")
  Member "aff wild2 extending the aff wild database for affect recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lossless attention in convolutional networks for facial expression recognition in the wild")
  Member "adversarial training in affective computing and sentiment analysis recent advances and perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "snore gans improving automatic snore sound classification with synthesized data")

Question: Please tell me which class the source paper 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)