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 "vision based teleoperation of shadow dexterous hand using end to end deep neural network"):
  Abstract: In this paper, we present TeachNet, a novel neural network architecture for intuitive and markerless vision-based teleoperation of dexterous robotic hands. Robot joint angles are directly generated from depth images of the human hand that produce visually similar robot hand poses in an end-to-end fashion. The special structure of TeachNet, combined with a consistency loss function, handles the differences in appearance and anatomy between human and robotic hands. A synchronized human-robot training set is generated from an existing dataset of labeled depth images of the human hand and simulated depth images of a robotic hand. The final training set includes 400K pairwise depth images and joint angles of a Shadow C6 robotic hand. The network evaluation results verify the superiority of TeachNet, especially regarding the high-precision condition. Imitation experiments and grasp tasks teleoperated by novice users demonstrate that TeachNet is more reliable and faster than the state-of-the-art vision-based teleoperation method.

Includes members:
  Member "intuitive hand teleoperation by novice operators using a continuous teleoperation subspace":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a continuous teleoperation subspace with empirical and algorithmic mapping algorithms for non anthropomorphic hands")
  Member "learning dexterous in hand manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "never give up learning directed exploration strategies")
  Member "neural kinematic networks for unsupervised motion retargetting":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "do as i do transferring human motion and appearance between monocular videos with spatial and temporal constraints")
  Member "v2v posenet voxel to voxel prediction network for accurate 3d hand and human pose estimation from a single depth map":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient convolutional neural networks for depth based multi person pose estimation")
  Member "region ensemble network improving convolutional network 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")
  Member "bighand2 2m benchmark hand pose dataset and state of the art analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "handaugment a simple data augmentation method for depth based 3d hand pose estimation")
  Member "deepprior improving fast and accurate 3d hand pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "3d hand pose estimation in the wild via graph refinement under adversarial learning")
  Member "time contrastive networks self supervised learning from video":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning precise 3d manipulation from multiple uncalibrated cameras")

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.RO(Robotics)