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: "towards combining htn planning and geometric task planning"
  Abstract: In this paper we present an interface between a symbolic planner and a geometric task planner, which is different to a standard trajectory planner in that the former is able to perform geometric reasoning on abstract entities---tasks. We believe that this approach facilitates a more principled interface to symbolic planning, while also leaving more room for the geometric planner to make independent decisions. We show how the two planners could be interfaced, and how their planning and backtracking could be interleaved. We also provide insights for a methodology for using the combined system, and experimental results to use as a benchmark with future extensions to both the combined system, as well as to the geometric task planner.

Connected through hyperedge HE-1 (induced by source paper "object centric task and motion planning in dynamic environments"):
  with members:
    - "continuous relaxation of symbolic planner for one shot imitation learning"
    - "sampling based methods for factored task and motion planning"
    - "combined task and motion planning as classical ai planning"
    - "depth based object tracking using a robust gaussian filter"
    - "probabilistic object tracking using a range camera"
    - "the fast downward planning system"

Connected through hyperedge HE-2 (induced by source paper "describing physics for physical reasoning force based sequential manipulation planning"):
  with members:
    - "probabilistic framework for constrained manipulations and task and motion planning under uncertainty"
    - "a quasi static model and simulation approach for pushing grasping and jamming"
    - "a data efficient approach to precise and controlled pushing"
    - "reactive planar manipulation with convex hybrid mpc"

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"
    - "backward forward search for manipulation 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:
    - "backward forward search for manipulation planning"
    - "a hybrid lp rpg heuristic for modelling numeric resource flows in 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 "admissible abstractions for near optimal task and motion planning"):
  with members:
    - "combined task and motion planning as classical ai planning"

Connected through hyperedge HE-6 (induced by source paper "conditional task and motion planning through an effort based approach"):
  with members:
    - "ffrob leveraging symbolic planning for efficient task and motion planning"
    - "grasp pose detection in point clouds"
    - "the ff planning system fast plan generation through heuristic search"

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.AI(Artificial Intelligence)