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 "two stream aural visual affect analysis in the wild"):
  Abstract: Human affect recognition is an essential part of natural human-computer interaction. However, current methods are still in their infancy, especially for in-the-wild data. In this work, we introduce our submission to the Affective Behavior Analysis in-the-wild (ABAW) 2020 competition. We propose a two-stream aural-visual analysis model to recognize affective behavior from videos. Audio and image streams are first processed separately and fed into a convolutional neural network. Instead of applying recurrent architectures for temporal analysis we only use temporal convolutions. Furthermore, the model is given access to additional features extracted during face-alignment. At training time, we exploit correlations between different emotion representations to improve performance. Our model achieves promising results on the challenging Aff-Wild2 database.

Includes members:
  Member "multi label class balancing algorithm for action unit detection":
    also connected through other hyperedges:
      (none)
  Member "analysing affective behavior in the first abaw 2020 competition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "m 3 t multi modal continuous valence arousal estimation in the wild")
  Member "fau facial expressions valence and arousal a multi task solution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unique class group based multi label balancing optimizer for action unit detection")
  Member "expression affect action unit recognition aff wild2 multi task learning and arcface":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lossless attention in convolutional networks for facial expression recognition in the wild")
  Member "context aware emotion recognition networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "emoticon context aware multimodal emotion recognition using frege s principle")
  Member "retinaface single stage dense face localisation in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generalizing face representation with unlabeled data")
  Member "face behavior a la carte expressions affect and action units in a single network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multitask emotion recognition with incomplete labels")
  Member "arcface additive angular margin loss for deep face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")

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)