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: "keto learning keypoint representations for tool manipulation"
  Abstract: We aim to develop an algorithm for robots to manipulate novel objects as tools for completing different task goals. An efficient and informative representation would facilitate the effectiveness and generalization of such algorithms. For this purpose, we present KETO, a framework of learning keypoint representations of tool-based manipulation. For each task, a set of task-specific keypoints is jointly predicted from 3D point clouds of the tool object by a deep neural network. These keypoints offer a concise and informative description of the object to determine grasps and subsequent manipulation actions. The model is learned from self-supervised robot interactions in the task environment without the need for explicit human annotations. We evaluate our framework in three manipulation tasks with tool use. Our model consistently outperforms state-of-the-art methods in terms of task success rates. Qualitative results of keypoint prediction and tool generation are shown to visualize the learned representations.

Connected through hyperedge HE-1 (induced by source paper "the surprising effectiveness of linear models for visual foresight in object pile manipulation"):
  with members:
    - "planning robot motion using deep visual prediction"
    - "unsupervised learning of object structure and dynamics from videos"
    - "object centric forward modeling for model predictive control"
    - "learning to manipulate object collections using grounded state representations"
    - "difftaichi differentiable programming for physical simulation"
    - "friction variability in planar pushing data anisotropic friction and data collection bias"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "a geometric perspective on visual imitation learning"):
  with members:
    - "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"
    - "robot eye hand coordination learning by watching human demonstrations a task function approximation approach"
    - "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-3 (induced by source paper "visual geometric skill inference by watching human demonstration"):
  with members:
    - "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"
    - "robot eye hand coordination learning by watching human demonstrations a task function approximation approach"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "neural motifs scene graph parsing with global context"

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)