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: "dexpilot vision based teleoperation of dexterous robotic hand arm system"
  Abstract: Teleoperation offers the possibility of imparting robotic systems with sophisticated reasoning skills, intuition, and creativity to perform tasks. However, current teleoperation solutions for high degree-of-actuation (DoA), multi-fingered robots are generally cost-prohibitive, while low-cost offerings usually provide reduced degrees of control. Herein, a low-cost, vision based teleoperation system, DexPilot, was developed that allows for complete control over the full 23 DoA robotic system by merely observing the bare human hand. DexPilot enables operators to carry out a variety of complex manipulation tasks that go beyond simple pick-and-place operations. This allows for collection of high dimensional, multi-modality, state-action data that can be leveraged in the future to learn sensorimotor policies for challenging manipulation tasks. The system performance was measured through speed and reliability metrics across two human demonstrators on a variety of tasks. The videos of the experiments can be found at https://sites.google.com/view/dex-pilot.

Connected through hyperedge HE-1 (induced by source paper "human grasp classification for reactive human to robot handovers"):
  with members:
    - "transferable task execution from pixels through deep planning domain learning"
    - "3d hand shape and pose estimation from a single rgb image"
    - "freihand a dataset for markerless capture of hand pose and shape from single rgb images"
    - "learning joint reconstruction of hands and manipulated objects"
    - "6 dof grasping for target driven object manipulation in clutter"
    - "representing robot task plans as robust logical dynamical systems"
    - "honnotate a method for 3d annotation of hand and objects poses"
    - "online replanning in belief space for partially observable task and motion problems"

Connected through hyperedge HE-2 (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"
    - "task oriented hand motion retargeting for dexterous manipulation imitation"
    - "vision based teleoperation of shadow dexterous hand using end to end deep neural network"
    - "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-3 (induced by source paper "motion reasoning for goal based imitation learning"):
  with members:
    - "representing robot task plans as robust logical dynamical systems"
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "scaling egocentric vision the epic kitchens dataset"
    - "explicability legibility predictability transparency privacy security the emerging landscape of interpretable agent behavior"
    - "zero shot visual imitation"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "beyond imitation zero shot task transfer on robots by learning concepts as cognitive programs"
    - "rmpflow a computational graph for automatic motion policy generation"

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.CV(Computer Vision and Pattern Recognition)