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 "deep facial expression recognition a survey"):
  Abstract: With the transition of facial expression recognition (FER) from laboratory-controlled to challenging in-the-wild conditions and the recent success of deep learning techniques in various fields, deep neural networks have increasingly been leveraged to learn discriminative representations for automatic FER. Recent deep FER systems generally focus on two important issues: overfitting caused by a lack of sufficient training data and expression-unrelated variations, such as illumination, head pose and identity bias. In this paper, we provide a comprehensive survey on deep FER, including datasets and algorithms that provide insights into these intrinsic problems. First, we describe the standard pipeline of a deep FER system with the related background knowledge and suggestions of applicable implementations for each stage. We then introduce the available datasets that are widely used in the literature and provide accepted data selection and evaluation principles for these datasets. For the state of the art in deep FER, we review existing novel deep neural networks and related training strategies that are designed for FER based on both static images and dynamic image sequences, and discuss their advantages and limitations. Competitive performances on widely used benchmarks are also summarized in this section. We then extend our survey to additional related issues and application scenarios. Finally, we review the remaining challenges and corresponding opportunities in this field as well as future directions for the design of robust deep FER systems.

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 "deep covariance descriptors for facial expression recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic facial expression generation on hilbert hypersphere with conditional wasserstein generative adversarial nets")
  Member "geometry contrastive gan for facial expression transfer":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cascade ef gan progressive facial expression editing with local focuses")
  Member "multi task multi label and multi domain learning with residual convolutional networks for emotion recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "beyond without forgetting multi task learning for classification with disjoint datasets")
  Member "covariance pooling for facial expression recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lossless attention in convolutional networks for facial expression recognition in the wild")
  Member "vgan based image representation learning for privacy preserving facial expression recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to augment expressions for few shot fine grained facial expression recognition")
  Member "accurate facial parts localization and deep learning for 3d facial expression recognition":
    also connected through other hyperedges:
      (none)
  Member "island loss for learning discriminative features in facial expression recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "region attention networks for pose and occlusion robust facial expression recognition")

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)