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 "upper middle and lower region learning for facial action unit detection"):
  Abstract: Facial action units (AUs) detection is fundamental to facial expression analysis. As AU occur only in a small area of face, region based learning has been widely recognized useful for AU detection. Most region based studies focus on a small region where the AU occurs. Focusing on a specific region is helpful in eliminating the influence of identity, but to be risk for losing information. It is difficult to find balance. In this study, I propose a simple strategy. I divide the face into three large regions, upper, middle and lower region, and group AUs based on where it occurs. I propose a new end-to-end deep learning framework named three regions based attention network (TRA-Net). After extracting the global feature, TRA-Net uses a hard attention module to extract three feature maps, each of which contains only a specific region. Each region-specific feature map is fed to an independent branch. For each branch, three continuous soft attention modules are used to extract higher-level features for final AU detection. In the DISFA dataset, this model achieves the highest F1 scores for the detection of AU1, AU2 and AU4, and produces the highest accuracy in comparison with the state-of-the-art methods.

Includes members:
  Member "spatio temporal relation and attention learning for facial action unit detection":
    also connected through other hyperedges:
      (none)
  Member "feratt facial expression recognition with attention net":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "slimconv reducing channel redundancy in convolutional neural networks by weights flipping")
  Member "semantic relationships guided representation learning for facial action unit recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spatio temporal relation and attention learning for facial action unit detection")
  Member "facial expression recognition using disentangled adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "analysing affective behavior in the first abaw 2020 competition")
  Member "deep structure inference network for facial action unit recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "jaa net joint facial action unit detection and face alignment via adaptive attention")
  Member "deep adaptive attention for joint facial action unit detection and face alignment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "region based adversarial synthesis of facial action units")
  Member "facial action unit detection using attention and relation learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "semi supervised unconstrained action unit detection via latent feature domain")
  Member "cbam convolutional block attention module":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning enriched features for real image restoration and enhancement")

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)