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: "dual arm in hand manipulation and regrasping using dexterous manipulation graphs"
  Abstract: This work focuses on the problem of in-hand manipulation and regrasping of objects with parallel grippers. We propose Dexterous Manipulation Graph (DMG) as a representation on which we define planning for in-hand manipulation and regrasping. The DMG is a disconnected undirected graph that represents the possible motions of a finger along the object's surface. We formulate the in-hand manipulation and regrasping problem as a graph search problem from the initial to the final configuration. The resulting plan is a sequence of coordinated in-hand pushing and regrasping movements. We propose a dual-arm system for the execution of the sequence where both hands are used interchangeably. We demonstrate our approach on an ABB Yumi robot tasked with different grasp reconfigurations.

Connected through hyperedge HE-1 (induced by source paper "learning hierarchical control for robust in hand manipulation"):
  with members:
    - "residual policy learning"
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "residual reinforcement learning for robot control"
    - "geometric in hand regrasp planning alternating optimization of finger gaits and in grasp manipulation"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "real time robust finger gaits planning under object shape and dynamics uncertainties"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-2 (induced by source paper "autonomous bimanual functional regrasping of novel object class instances"):
  with members:
    - "transferring category based functional grasping skills by latent space non rigid registration"
    - "light weight refinenet for real time semantic segmentation"
    - "geometric in hand regrasp planning alternating optimization of finger gaits and in grasp manipulation"
    - "supervised autonomous locomotion and manipulation for disaster response with a centaur like robot"
    - "learning postural synergies for categorical grasping through shape space registration"
    - "autonomous dual arm manipulation of familiar objects"
    - "transferring grasping skills to novel instances by latent space non rigid registration"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"

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)