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: "task oriented hand motion retargeting for dexterous manipulation imitation"
  Abstract: Human hand actions are quite complex, especially when they involve object manipulation, mainly due to the high dimensionality of the hand and the vast action space that entails. Imitating those actions with dexterous hand models involves different important and challenging steps: acquiring human hand information, retargeting it to a hand model, and learning a policy from acquired data. In this work, we capture the hand information by using a state-of-the-art hand pose estimator. We tackle the retargeting problem from the hand pose to a 29 DoF hand model by combining inverse kinematics and PSO with a task objective optimisation. This objective encourages the virtual hand to accomplish the manipulation task, relieving the effect of the estimator's noise and the domain gap. Our approach leads to a better success rate in the grasping task compared to our inverse kinematics baseline, allowing us to record successful human demonstrations. Furthermore, we used these demonstrations to learn a policy network using generative adversarial imitation learning (GAIL) that is able to autonomously grasp an object in the virtual space.

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"
    - "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-2 (induced by source paper "solving rubik s cube with a robot hand"):
  with members:
    - "sampling based planning of in hand manipulation with external pushes"
    - "active domain randomization"
    - "meta reinforcement learning with task embedding and shared policy"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "distilling policy distillation"
    - "deep dynamics models for learning dexterous manipulation"
    - "meta reinforcement learning as task inference"
    - "deceptionnet network driven domain randomization"

Connected through hyperedge HE-3 (induced by source paper "patch based 3d human pose refinement"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "monocular 3d human pose estimation by generation and ordinal ranking"
    - "self supervised learning of 3d human pose using multi view geometry"
    - "ordinal depth supervision for 3d human pose estimation"
    - "rethinking pose in 3d multi stage refinement and recovery for markerless motion capture"
    - "posefix model agnostic general human pose refinement network"
    - "bodynet volumetric inference of 3d human body shapes"
    - "learning to refine human pose estimation"

Connected through hyperedge HE-4 (induced by source paper "active 6d multi object pose estimation in cluttered scenarios with deep reinforcement learning"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "instance and category level 6d object pose estimation"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "bop benchmark for 6d object pose estimation"
    - "geometry aware recurrent neural networks for active visual recognition"
    - "multi task deep networks for depth based 6d object pose and joint registration in crowd scenarios"
    - "pvnet pixel wise voting network for 6dof pose estimation"
    - "deep object centric policies for autonomous driving"

Connected through hyperedge HE-5 (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"
    - "vision based teleoperation of shadow dexterous hand using end to end deep neural network"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "robust learning of tactile force estimation through robot interaction"

Connected through hyperedge HE-6 (induced by source paper "hands18 methods techniques and applications for hand observation"):
  with members:
    - "hand tremor frequency estimation in videos"
    - "hand pose estimation via latent 2 5d heatmap regression"
    - "numerical coordinate regression with convolutional neural networks"
    - "occlusion aware hand pose estimation using hierarchical mixture density network"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "cascaded pyramid network for multi person pose estimation"
    - "ganerated hands for real time 3d hand tracking from monocular rgb"
    - "real time hand tracking under occlusion from an egocentric rgb d sensor"

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)