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: "robot eye hand coordination learning by watching human demonstrations a task function approximation approach"
  Abstract: We present a robot eye-hand coordination learning method that can directly learn visual task specification by watching human demonstrations. Task specification is represented as a task function, which is learned using inverse reinforcement learning(IRL\cite{Abbeel2004}) by inferring differential rewards between state changes. The learned task function is then used as continuous feedbacks in an uncalibrated visual servoing(UVS\cite{Jagersand1997}) controller designed for the execution phase. Our proposed method can directly learn from raw videos, which removes the need for hand-engineered task specification. It can also provide task interpretability by directly approximating the task function. Besides, benefiting from the use of a traditional UVS controller, our training process is efficient and the learned policy is independent from a particular robot platform. Various experiments were designed to show that, for a certain DOF task, our method can adapt to task/environment variances in target positions, backgrounds, illuminations, and occlusions without prior retraining.

Connected through hyperedge HE-1 (induced by source paper "a geometric perspective on visual imitation learning"):
  with members:
    - "keto learning keypoint representations for tool manipulation"
    - "visual geometric skill inference by watching human demonstration"
    - "self supervised correspondence in visuomotor policy learning"
    - "graph structured visual imitation"
    - "third person visual imitation learning via decoupled hierarchical controller"
    - "zero shot visual imitation"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "one shot visual imitation learning via meta learning"

Connected through hyperedge HE-2 (induced by source paper "visual geometric skill inference by watching human demonstration"):
  with members:
    - "keto learning keypoint representations for tool manipulation"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "deep reinforcement learning for industrial insertion tasks with visual inputs and natural reward signals"
    - "synthetically trained neural networks for learning human readable plans from real world demonstrations"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "online object and task learning via human robot interaction"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "neural motifs scene graph parsing with global context"

Connected through hyperedge HE-3 (induced by source paper "evaluation of state representation methods in robot hand eye coordination learning from demonstration"):
  with members:
    - "state representation learning for control an overview"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "mocogan decomposing motion and content for video generation"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "embed to control a locally linear latent dynamics model for control from raw images"
    - "deep spatial autoencoders for visuomotor learning"
    - "deep learning of representations looking forward"

Connected through hyperedge HE-4 (induced by source paper "long range teleoperation for fine manipulation tasks under time delay network conditions"):
  with members:
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "incremental 3d line segments extraction from semi dense slam"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-5 (induced by source paper "online object and task learning via human robot interaction"):
  with members:
    - "few shot image recognition by predicting parameters from activations"
    - "incremental learning for robot perception through hri"
    - "yolo9000 better faster stronger"
    - "real world object recognition with off the shelf deep conv nets how many objects can icub learn"
    - "towards open set deep networks"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "learning deep features for discriminative localization"

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)