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 "cu net coupled u nets"):
  Abstract: We design a new connectivity pattern for the U-Net architecture. Given several stacked U-Nets, we couple each U-Net pair through the connections of their semantic blocks, resulting in the coupled U-Nets (CU-Net). The coupling connections could make the information flow more efficiently across U-Nets. The feature reuse across U-Nets makes each U-Net very parameter efficient. We evaluate the coupled U-Nets on two benchmark datasets of human pose estimation. Both the accuracy and model parameter number are compared. The CU-Net obtains comparable accuracy as state-of-the-art methods. However, it only has at least 60% fewer parameters than other approaches.

Includes members:
  Member "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learn to augment joint data augmentation and network optimization for text recognition")
  Member "a generative adversarial approach for zero shot learning from noisy texts":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "variational hetero encoder randomized gans for joint image text modeling")
  Member "adversarial posenet a structure aware convolutional network for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lightweight 3d human pose estimation network training using teacher student learning")
  Member "stacked hourglass networks for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "recurrent human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "augmented parallel pyramid net for attention guided pose estimation")
  Member "human pose estimation using deep consensus voting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "toward fast and accurate human pose estimation via soft gated skip connections")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "deepcut joint subset partition and labeling for multi person pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "neural human video rendering joint learning of dynamic textures and rendering to video translation")

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)