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: "backward forward search for manipulation planning"
  Abstract: In this paper we address planning problems in high-dimensional hybrid configuration spaces, with a particular focus on manipulation planning problems involving many objects. We present the hybrid backward-forward (HBF) planning algorithm that uses a backward identification of constraints to direct the sampling of the infinite action space in a forward search from the initial state towards a goal configuration. The resulting planner is probabilistically complete and can effectively construct long manipulation plans requiring both prehensile and nonprehensile actions in cluttered environments.

Connected through hyperedge HE-1 (induced by source paper "scalable and probabilistically complete planning for robotic spatial extrusion"):
  with members:
    - "freeform assembly planning"
    - "automated sequence and motion planning for robotic spatial extrusion of 3d trusses"
    - "sampling based methods for factored task and motion planning"
    - "probabilistic completeness of rrt for geometric and kinodynamic planning with forward propagation"
    - "complexity results and fast methods for optimal tabletop rearrangement with overhand grasps"
    - "sampling based algorithms for optimal motion planning"
    - "the fast downward planning system"

Connected through hyperedge HE-2 (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"
    - "developing and comparing single arm and dual arm regrasp"
    - "finding a needle in an exponential haystack discrete rrt for exploration of implicit roadmaps in multi robot motion planning"

Connected through hyperedge HE-3 (induced by source paper "sampling based methods for factored task and motion planning"):
  with members:
    - "ffrob leveraging symbolic planning for efficient task and motion planning"
    - "strips planning in infinite domains"
    - "a unifying formalism for shortest path problems with expensive edge evaluations via lazy best first search over paths with edge selectors"
    - "towards combining htn planning and geometric task planning"
    - "sampling based algorithms for optimal motion planning"
    - "the fast downward planning system"
    - "the ff planning system fast plan generation through heuristic search"

Connected through hyperedge HE-4 (induced by source paper "ffrob leveraging symbolic planning for efficient task and motion planning"):
  with members:
    - "a hybrid lp rpg heuristic for modelling numeric resource flows in planning"
    - "towards combining htn planning and geometric task planning"
    - "sampling based algorithms for optimal motion planning"
    - "the fast downward planning system"
    - "the ff planning system fast plan generation through heuristic search"

Connected through hyperedge HE-5 (induced by source paper "task centric optimization of configurations for assistive robots"):
  with members:
    - "in home and remote use of robotic body surrogates by people with profound motor deficits"

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)