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: "hypothesis based belief planning for dexterous grasping"
  Abstract: Belief space planning is a viable alternative to formalise partially observable control problems and, in the recent years, its application to robot manipulation problems has grown. However, this planning approach was tried successfully only on simplified control problems. In this paper, we apply belief space planning to the problem of planning dexterous reach-to-grasp trajectories under object pose uncertainty. In our framework, the robot perceives the object to be grasped on-the-fly as a point cloud and compute a full 6D, non-Gaussian distribution over the object's pose (our belief space). The system has no limitations on the geometry of the object, i.e., non-convex objects can be represented, nor assumes that the point cloud is a complete representation of the object. A plan in the belief space is then created to reach and grasp the object, such that the information value of expected contacts along the trajectory is maximised to compensate for the pose uncertainty. If an unexpected contact occurs when performing the action, such information is used to refine the pose distribution and triggers a re-planning. Experimental results show that our planner (IR3ne) improves grasp reliability and compensates for the pose uncertainty such that it doubles the proportion of grasps that succeed on a first attempt.

Connected through hyperedge HE-1 (induced by source paper "grasping and manipulation with a multi fingered hand"):
  with members:
    - "let s push things forward a survey on robot pushing"
    - "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"
    - "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 "aging touch systematic and unbiased presentation of tactile stimuli"):
  with members:
    - "grasping and manipulation with a multi fingered hand"
    - "2d linear time variant controller for human s intention detection for reach to grasp trajectories in novel scenes"
    - "metrics and benchmarks for remote shared controllers in industrial applications"
    - "automatic detection of myocontrol failures based upon situational context information"
    - "feature based transfer learning for robotic push manipulation"

Connected through hyperedge HE-3 (induced by source paper "robust and fast generation of top and side grasps for unknown objects"):
  with members:
    - "a novel geometry based algorithm for robust grasping in extreme clutter environment"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "reducing the barrier to entry of complex robotic software a moveit case study"
    - "deep learning for detecting robotic grasps"

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:
    - "let s push things forward a survey on robot pushing"

Connected through hyperedge HE-5 (induced by source paper "metrics and benchmarks for remote shared controllers in industrial applications"):
  with members:
    - "let s push things forward a survey on robot pushing"

Connected through hyperedge HE-6 (induced by source paper "automatic detection of myocontrol failures based upon situational context information"):
  with members:
    - "feature based transfer learning for robotic push manipulation"

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)