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 "probabilistic attribute tree in convolutional neural networks for facial expression recognition"):
  Abstract: In this paper, we proposed a novel Probabilistic Attribute Tree-CNN (PAT-CNN) to explicitly deal with the large intra-class variations caused by identity-related attributes, e.g., age, race, and gender. Specifically, a novel PAT module with an associated PAT loss was proposed to learn features in a hierarchical tree structure organized according to attributes, where the final features are less affected by the attributes. Then, expression-related features are extracted from leaf nodes. Samples are probabilistically assigned to tree nodes at different levels such that expression-related features can be learned from all samples weighted by probabilities. We further proposed a semi-supervised strategy to learn the PAT-CNN from limited attribute-annotated samples to make the best use of available data. Experimental results on five facial expression datasets have demonstrated that the proposed PAT-CNN outperforms the baseline models by explicitly modeling attributes. More impressively, the PAT-CNN using a single model achieves the best performance for faces in the wild on the SFEW dataset, compared with the state-of-the-art methods using an ensemble of hundreds of CNNs.

Includes members:
  Member "multi region ensemble convolutional neural network for facial expression recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "moel mixture of empathetic listeners")
  Member "island loss for learning discriminative features in facial expression recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "region attention networks for pose and occlusion robust facial expression recognition")
  Member "facenet2expnet regularizing a deep face recognition net for expression recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint deep learning of facial expression synthesis and recognition")
  Member "going deeper in facial expression recognition using deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint deep learning of facial expression synthesis and recognition")
  Member "lomo latent ordinal model for facial analysis in videos":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "frame attention networks for facial expression recognition in videos")
  Member "peak piloted deep network for facial expression recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "joint deep learning of facial expression synthesis and recognition")
  Member "an all in one convolutional neural network for face analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hierarchical binary cnns for landmark localization with limited resources")
  Member "joint unsupervised learning of deep representations and image clusters":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")

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)