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: "admissible abstractions for near optimal task and motion planning"
  Abstract: We define an admissibility condition for abstractions expressed using angelic semantics and show that these conditions allow us to accelerate planning while preserving the ability to find the optimal motion plan. We then derive admissible abstractions for two motion planning domains with continuous state. We extract upper and lower bounds on the cost of concrete motion plans using local metric and topological properties of the problem domain. These bounds guide the search for a plan while maintaining performance guarantees. We show that abstraction can dramatically reduce the complexity of search relative to a direct motion planner. Using our abstractions, we find near-optimal motion plans in planning problems involving $10^{13}$ states without using a separate task planner.

Connected through hyperedge HE-1 (induced by source paper "reactive navigation in partially familiar planar environments using semantic perceptual feedback"):
  with members:
    - "combining optimal control and learning for visual navigation in novel environments"
    - "conceptualization of object compositions using persistent homology"
    - "visual memory for robust path following"
    - "yolov3 an incremental improvement"
    - "sampling based methods for factored task and motion planning"
    - "fast autonomous flight in gps denied and cluttered environments"
    - "6 dof object pose from semantic keypoints"
    - "cognitive mapping and planning for visual navigation"

Connected through hyperedge HE-2 (induced by source paper "anytime multi arm task and motion planning for pick and place of individual objects via handoffs"):
  with members:
    - "drrt scalable and informed asymptotically optimal multi robot motion planning"
    - "towards robust product packing with a minimalistic end effector"
    - "fast high quality dual arm rearrangement in synchronous monotone tabletop setups"
    - "ffrob leveraging symbolic planning for efficient task and motion planning"
    - "scalable asymptotically optimal multi robot motion planning"
    - "analysis and observations from the first amazon picking challenge"
    - "finding a needle in an exponential haystack discrete rrt for exploration of implicit roadmaps in multi robot motion planning"
    - "on the power of manifold samples in exploring configuration spaces and the dimensionality of narrow passages"

Connected through hyperedge HE-3 (induced by source paper "pushing the boundaries of asymptotic optimality for sampling based roadmaps in motion and task planning"):
  with members:
    - "fast high quality dual arm rearrangement in synchronous monotone tabletop setups"
    - "ffrob leveraging symbolic planning for efficient task and motion planning"
    - "the critical radius in sampling based motion planning"
    - "new perspective on sampling based motion planning via random geometric graphs"
    - "deterministic sampling based motion planning optimality complexity and performance"
    - "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.AI(Artificial Intelligence)