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: "a framework for fine robotic assembly"
  Abstract: Fine robotic assembly, in which the parts to be assembled are small and fragile and lie in an unstructured environment, is still out of reach of today's industrial robots. The main difficulties arise in the precise localization of the parts in an unstructured environment and the control of contact interactions. Our contribution in this paper is twofold. First, we propose a taxonomy of the manipulation primitives that are specifically involved in fine assembly. Such a taxonomy is crucial for designing a scalable robotic system (both hardware and software) given the complexity of real-world assembly tasks. Second, we present a hardware and software architecture where we have addressed, in an integrated way, a number of issues arising in fine assembly, such as workspace optimization, external wrench compensation, position-based force control, etc. Finally, we show the above taxonomy and architecture in action on a highly dexterous task -- bimanual pin insertion -- which is one of the key steps in our long term project, the autonomous assembly of an IKEA chair.

Connected through hyperedge HE-1 (induced by source paper "improving robot success detection using static object data"):
  with members:
    - "robocse robot common sense embedding"
    - "scaling egocentric vision the epic kitchens dataset"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "shapestacks learning vision based physical intuition for generalised object stacking"
    - "shifting the baseline single modality performance on visual navigation qa"
    - "visual curiosity learning to ask questions to learn visual recognition"
    - "a simple neural network module for relational reasoning"
    - "learning interpretable spatial operations in a rich 3d blocks world"

Connected through hyperedge HE-2 (induced by source paper "hybrid differential dynamic programming for planar manipulation primitives"):
  with members:
    - "tactile dexterity manipulation primitives with tactile feedback"
    - "in hand manipulation via motion cones"
    - "reactive planar manipulation with convex hybrid mpc"
    - "hybrid control trajectory optimization under uncertainty"
    - "feedback control of the pusher slider system a story of hybrid and underactuated contact dynamics"

Connected through hyperedge HE-3 (induced by source paper "closed chain manipulation of large objects by multi arm robotic systems"):
  with members:
    - "a single query manipulation planner"

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)