human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "a mobile robot hand arm teleoperation system by vision and imu"):
  with members:
    - "avid learning multi stage tasks via pixel level translation of human videos"
    - "third person visual imitation learning via decoupled hierarchical controller"
    - "dexpilot vision based teleoperation of dexterous robotic hand arm system"
    - "task oriented hand motion retargeting for dexterous manipulation imitation"
    - "toward multimodal image to image translation"
    - "image to image translation with conditional adversarial networks"
    - "context encoders feature learning by inpainting"
    - "spatial transformer networks"

Connected through hyperedge HE-2 (induced by source paper "learn and transfer knowledge of preferred assistance strategies in semi autonomous telemanipulation"):
  with members:
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "human robot mutual adaptation in shared autonomy"

Connected through hyperedge HE-3 (induced by source paper "graph structured visual imitation"):
  with members:
    - "learning actionable representations from visual observations"
    - "zero shot visual imitation"
    - "graph networks as learnable physics engines for inference and control"
    - "state representation learning for control an overview"
    - "sfv reinforcement learning of physical skills from videos"
    - "relational inductive biases deep learning and graph networks"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "combining self supervised learning and imitation for vision based rope manipulation"

Connected through hyperedge HE-4 (induced by source paper "dexpilot vision based teleoperation of dexterous robotic hand arm system"):
  with members:
    - "making convolutional networks shift invariant again"
    - "learning joint reconstruction of hands and manipulated objects"
    - "learning latent plans from play"
    - "generalized feedback loop for joint hand object pose estimation"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "task oriented hand motion retargeting for dexterous manipulation imitation"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "robust learning of tactile force estimation through robot interaction"

Connected through hyperedge HE-5 (induced by source paper "real time approximate bayesian computation for scene understanding"):
  with members:
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "approximate bayesian image interpretation using generative probabilistic graphics programs"

Connected through hyperedge HE-6 (induced by source paper "a continuous teleoperation subspace with empirical and algorithmic mapping algorithms for non anthropomorphic hands"):
  with members:
    - "emg controlled hand teleoperation using a continuous teleoperation subspace"
    - "intuitive hand teleoperation by novice operators using a continuous teleoperation subspace"

Question: Please tell me which class the center node 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)