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: "let s push things forward a survey on robot pushing"
  Abstract: As robots make their way out of factories into human environments, outer space, and beyond, they require the skill to manipulate their environment in multifarious, unforeseeable circumstances. With this regard, pushing is an essential motion primitive that dramatically extends a robot's manipulation repertoire. In this work, we review the robotic pushing literature. While focusing on work concerned with predicting the motion of pushed objects, we also cover relevant applications of pushing for planning and control. Beginning with analytical approaches, under which we also subsume physics engines, we then proceed to discuss work on learning models from data. In doing so, we dedicate a separate section to deep learning approaches which have seen a recent upsurge in the literature. Concluding remarks and further research perspectives are given at the end of the paper.

Connected through hyperedge HE-1 (induced by source paper "grasping and manipulation with a multi fingered hand"):
  with members:
    - "multisensory learning framework for robot drumming"
    - "robust and fast generation of top and side grasps for unknown objects"
    - "metrics and benchmarks for remote shared controllers in industrial applications"
    - "automatic detection of myocontrol failures based upon situational context information"
    - "hypothesis based belief planning for dexterous grasping"
    - "feature based transfer learning for robotic push manipulation"
    - "online planning algorithms for pomdps"
    - "perseus randomized point based value iteration for pomdps"

Connected through hyperedge HE-2 (induced by source paper "accurate vision based manipulation through contact reasoning"):
  with members:
    - "combining physical simulators and object based networks for control"
    - "combining coarse and fine physics for manipulation using parallel in time integration"
    - "efficient humanoid contact planning using learned centroidal dynamics prediction"
    - "robustness via retrying closed loop robotic manipulation with self supervised learning"
    - "in hand manipulation via motion cones"
    - "self supervised visual planning with temporal skip connections"
    - "reactive planar manipulation with convex hybrid mpc"
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"

Connected through hyperedge HE-3 (induced by source paper "embracing contact pushing multiple objects with robot s forearm"):
  with members:
    - "deep visual foresight for planning robot motion"

Connected through hyperedge HE-4 (induced by source paper "2d linear time variant controller for human s intention detection for reach to grasp trajectories in novel scenes"):
  with members:
    - "hypothesis based belief planning for dexterous grasping"

Connected through hyperedge HE-5 (induced by source paper "metrics and benchmarks for remote shared controllers in industrial applications"):
  with members:
    - "hypothesis based belief planning for dexterous grasping"

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)