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 "persemon a deep network for joint analysis of apparent personality emotion and their relationship"):
  Abstract: Personality and emotion are both central to affective computing. Existing works solve them individually. In this paper we investigate if such high-level affect traits and their relationship can be jointly learned from face images in the wild. To this end, we introduce PersEmoN, an end-to-end trainable and deep Siamese-like network which we call emotion network and personality network, respectively. It consists of two convolutional network branches, one for emotion and the other for apparent personality. Both networks share their bottom feature extraction module and are optimized within a multi-task learning framework. Emotion and personality networks are dedicated to their own annotated dataset. An adversarial-like loss function is further employed to promote representation coherence among heterogeneous dataset sources. Based on this, the emotion-to-personality relationship is also well explored. Extensive experiments are provided to demonstrate the effectiveness of PersEmoN.

Includes members:
  Member "affectnet a database for facial expression valence and arousal computing in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cascade ef gan progressive facial expression editing with local focuses")
  Member "first impressions a survey on vision based apparent personality trait analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "machines learn appearance bias in face recognition")
  Member "explaining first impressions modeling recognizing and explaining apparent personality from videos":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust regression via deep negative correlation learning")
  Member "sphereface deep hypersphere embedding for face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "facial affect estimation in the wild using deep residual and convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bounded residual gradient networks breg net for facial affect computing")
  Member "emotion recognition in the wild using deep neural networks and bayesian classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "group emotion recognition using machine learning")
  Member "bi modal first impressions recognition using temporally ordered deep audio and stochastic visual features":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust regression via deep negative correlation learning")
  Member "deep impression audiovisual deep residual networks for multimodal apparent personality trait recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "robust regression via deep negative correlation learning")

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)