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 "thin slicing network a deep structured model for pose estimation in videos"):
  Abstract: Deep ConvNets have been shown to be effective for the task of human pose estimation from single images. However, several challenging issues arise in the video-based case such as self-occlusion, motion blur, and uncommon poses with few or no examples in training data sets. Temporal information can provide additional cues about the location of body joints and help to alleviate these issues. In this paper, we propose a deep structured model to estimate a sequence of human poses in unconstrained videos. This model can be efficiently trained in an end-to-end manner and is capable of representing appearance of body joints and their spatio-temporal relationships simultaneously. Domain knowledge about the human body is explicitly incorporated into the network providing effective priors to regularize the skeletal structure and to enforce temporal consistency. The proposed end-to-end architecture is evaluated on two widely used benchmarks (Penn Action dataset and JHMDB dataset) for video-based pose estimation. Our approach significantly outperforms the existing state-of-the-art methods.

Includes members:
  Member "convolutional pose machines":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "stacked hourglass networks for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "pose for action action for pose":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unipose unified human pose estimation in single images and videos")
  Member "structured feature learning for pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "repose learning deep kinematic priors for fast human pose estimation")
  Member "chained predictions using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unipose unified human pose estimation in single images and videos")
  Member "conditional random fields as recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "real time high performance semantic image segmentation of urban street scenes")
  Member "deepcut joint subset partition and labeling for multi person pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neural human video rendering joint learning of dynamic textures and rendering to video translation")
  Member "flowing convnets for human pose estimation in videos":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hierarchical binary cnns for landmark localization with limited resources")

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)