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: "intuitive hand teleoperation by novice operators using a continuous teleoperation subspace"
  Abstract: Human-in-the-loop manipulation is useful in when autonomous grasping is not able to deal sufficiently well with corner cases or cannot operate fast enough. Using the teleoperator's hand as an input device can provide an intuitive control method but requires mapping between pose spaces which may not be similar. We propose a low-dimensional and continuous teleoperation subspace which can be used as an intermediary for mapping between different hand pose spaces. We present an algorithm to project between pose space and teleoperation subspace. We use a non-anthropomorphic robot to experimentally prove that it is possible for teleoperation subspaces to effectively and intuitively enable teleoperation. In experiments, novice users completed pick and place tasks significantly faster using teleoperation subspace mapping than they did using state of the art teleoperation methods.

Connected through hyperedge HE-1 (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"
    - "vision based teleoperation of shadow dexterous hand using end to end deep neural network"

Connected through hyperedge HE-2 (induced by source paper "underactuation design for tendon driven hands via optimization of mechanically realizable manifolds in posture and torque spaces"):
  with members:
    - "underactuated hand design using mechanically realizable manifolds"

Connected through hyperedge HE-3 (induced by source paper "vision based teleoperation of shadow dexterous hand using end to end deep neural network"):
  with members:
    - "learning dexterous in hand manipulation"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "v2v posenet voxel to voxel prediction network for accurate 3d hand and human pose estimation from a single depth map"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"
    - "deepprior improving fast and accurate 3d hand pose estimation"
    - "time contrastive networks self supervised learning from video"
    - "depth based 3d hand pose estimation from current achievements to future goals"

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)