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 "fau facial expressions valence and arousal a multi task solution"):
  Abstract: In the paper, we aim to train a unified model that performs three tasks: Facial Action Units (FAU) prediction, seven basic facial expressions prediction, as well as valence and arousal prediction. The main challenge of this task is the lack of fully-annotated dataset. Most of existing datasets only contain one or two types of labels. To tackle this challenge, we propose an algorithm for the multitask model to learn from partial labels. The algorithm has two steps: first, we train a teacher model to perform all three tasks, where each instance is trained by the ground truth label of its corresponding task. Second, we refer to the outputs of the teacher model as the soft labels. We use the soft labels and the ground truths to train the student model. We find that the student model outperforms the teacher model on all the tasks, possibly due to the exposure to the full set of labels. Finally, we use ensemble modeling to boost the performance further on the three tasks.

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 "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lossless attention in convolutional networks for facial expression recognition in the wild")
  Member "a multi component cnn rnn approach for dimensional emotion recognition in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "lossless attention in convolutional networks for facial expression recognition in the wild")
  Member "from facial expression recognition to interpersonal relation prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ae ot gan training gans from data specific latent distribution")

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)