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 "hand3d hand pose estimation using 3d neural network"):
  Abstract: We propose a novel 3D neural network architecture for 3D hand pose estimation from a single depth image. Different from previous works that mostly run on 2D depth image domain and require intermediate or post process to bring in the supervision from 3D space, we convert the depth map to a 3D volumetric representation, and feed it into a 3D convolutional neural network(CNN) to directly produce the pose in 3D requiring no further process. Our system does not require the ground truth reference point for initialization, and our network architecture naturally integrates both local feature and global context in 3D space. To increase the coverage of the hand pose space of the training data, we render synthetic depth image by transferring hand pose from existing real image datasets. We evaluation our algorithm on two public benchmarks and achieve the state-of-the-art performance. The synthetic hand pose dataset will be available.

Includes members:
  Member "training a feedback loop for hand pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deepim deep iterative matching for 6d pose estimation")
  Member "robust 3d hand pose estimation in single depth images from single view cnn to multi view cnns":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rotation invariant mixed graphical model network for 2d hand pose estimation")
  Member "deepcontext context encoding neural pathways for 3d holistic scene understanding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "indoor scene understanding in 2 5 3d for autonomous agents a survey")
  Member "deep sliding shapes for amodal 3d object detection in rgb d images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "hands deep in deep learning for hand pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fast and accurate 3d hand pose estimation via recurrent neural network for capturing hand articulations")

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)