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 "a fine grained facial expression database for end to end multi pose facial expression recognition"):
  Abstract: The recent research of facial expression recognition has made a lot of progress due to the development of deep learning technologies, but some typical challenging problems such as the variety of rich facial expressions and poses are still not resolved. To solve these problems, we develop a new Facial Expression Recognition (FER) framework by involving the facial poses into our image synthesizing and classification process. There are two major novelties in this work. First, we create a new facial expression dataset of more than 200k images with 119 persons, 4 poses and 54 expressions. To our knowledge this is the first dataset to label faces with subtle emotion changes for expression recognition purpose. It is also the first dataset that is large enough to validate the FER task on unbalanced poses, expressions, and zero-shot subject IDs. Second, we propose a facial pose generative adversarial network (FaPE-GAN) to synthesize new facial expression images to augment the data set for training purpose, and then learn a LightCNN based Fa-Net model for expression classification. Finally, we advocate four novel learning tasks on this dataset. The experimental results well validate the effectiveness of the proposed approach.

Includes members:
  Member "deep emotion facial expression recognition using attentional convolutional network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to augment expressions for few shot fine grained facial expression recognition")
  Member "local learning with deep and handcrafted features for facial expression recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to augment expressions for few shot fine grained facial expression recognition")
  Member "heterogeneous knowledge transfer in video emotion recognition attribution and summarization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to augment expressions for few shot fine grained facial expression recognition")
  Member "hydraplus net attentive deep features for pedestrian analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "magnifiernet towards semantic regularization and fusion for person re identification")
  Member "pose normalized image generation for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "triplet online instance matching loss for person re identification")
  Member "going deeper in facial expression recognition using deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "joint deep learning of facial expression synthesis and recognition")
  Member "joint face detection and alignment using multitask cascaded convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "do deep neural networks learn facial action units when doing expression recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning to augment expressions for few shot fine grained 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)