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: "developing and comparing single arm and dual arm regrasp"
  Abstract: The goal of this letter is to develop efficient regrasp algorithms for single-arm and dual-arm regrasp and compares the performance of single-arm and dual-arm regrasp by running the two algorithms thousands of times. We focus on pick-and-place regrasp, which reorients an object from one placement to another by using a sequence of pick-ups and place-downs. After analyzing the simulation results, we find dual-arm regrasp is not necessarily better than single-arm regrasp: dual-arm regrasp is flexible. When the two hands can grasp the object with good clearance, dual-arm regrasp is better and has higher successful rate than single-arm regrasp. When the grasps overlap, dual-arm regrasp is bad. Developers need to sample grasps with high density to reduce overlapping. Following the results, practitioners may choose single-arm or dual-arm robots by considering the object shapes and overlapping of grasps. Or they can run our algorithms and see the statistical results before making decisions.

Connected through hyperedge HE-1 (induced by source paper "autonomous bimanual functional regrasping of novel object class instances"):
  with members:
    - "dual arm in hand manipulation and regrasping using dexterous manipulation graphs"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "dual arm in hand manipulation and regrasping using dexterous manipulation graphs"):
  with members:
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation graphs"
    - "pneumatic shape shifting fingers to reorient and grasp"
    - "in hand manipulation via motion cones"
    - "preparatory manipulation planning using automatically determined single and dual arms"
    - "reinforcement learning for pivoting task"
    - "closed chain manipulation of large objects by multi arm robotic systems"
    - "data driven grasp synthesis a survey"

Connected through hyperedge HE-3 (induced by source paper "tethered tool manipulation planning with cable maneuvering"):
  with members:
    - "reinforcement learning in topology based representation for human body movement with whole arm manipulation"
    - "arm manipulation planning of tethered tools with the help of a tool balancer"
    - "learning sampling distributions for robot motion planning"
    - "manipulation planning under changing external forces"
    - "deep visual foresight for planning robot motion"
    - "backward forward search for manipulation planning"
    - "finding a needle in an exponential haystack discrete rrt for exploration of implicit roadmaps in multi robot motion planning"

Connected through hyperedge HE-4 (induced by source paper "human in the loop robotic manipulation planning for collaborative assembly"):
  with members:
    - "combined task and motion planning for a dual arm robot to use a suction cup tool"
    - "dexterous manipulation graphs"
    - "manipulation planning under changing external forces"

Connected through hyperedge HE-5 (induced by source paper "dual arm assembly planning considering gravitational constraints"):
  with members:
    - "manipulation planning under changing external forces"
    - "assembly sequence planning for motion planning"

Connected through hyperedge HE-6 (induced by source paper "arm manipulation planning of tethered tools with the help of a tool balancer"):
  with members:
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "tactile regrasp grasp adjustments via simulated tactile transformations"
    - "regrasp planning considering bipedal stability constraints"
    - "robotic manipulation of a rotating chain"

Connected through hyperedge HE-7 (induced by source paper "preparatory manipulation planning using automatically determined single and dual arms"):
  with members:
    - "closed chain manipulation of large objects by multi arm robotic systems"

Connected through hyperedge HE-8 (induced by source paper "a certified complete bimanual manipulation planner"):
  with members:
    - "closed chain manipulation of large objects by multi arm robotic systems"
    - "completeness of randomized kinodynamic planners with state based steering"
    - "analyzing the utility of a support pin in sequential robotic manipulation"
    - "a single query manipulation planner"
    - "a general fast and robust implementation of the time optimal path parameterization algorithm"
    - "sampling based algorithms for optimal motion planning"

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)