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: "accelerating partial order planners some techniques for effective search control and pruning"
  Abstract: We propose some domain-independent techniques for bringing well-founded partial-order planners closer to practicality. The first two techniques are aimed at improving search control while keeping overhead costs low. One is based on a simple adjustment to the default A* heuristic used by UCPOP to select plans for refinement. The other is based on preferring ``zero commitment'' (forced) plan refinements whenever possible, and using LIFO prioritization otherwise. A more radical technique is the use of operator parameter domains to prune search. These domains are initially computed from the definitions of the operators and the initial and goal conditions, using a polynomial-time algorithm that propagates sets of constants through the operator graph, starting in the initial conditions. During planning, parameter domains can be used to prune nonviable operator instances and to remove spurious clobbering threats. In experiments based on modifications of UCPOP, our improved plan and goal selection strategies gave speedups by factors ranging from 5 to more than 1000 for a variety of problems that are nontrivial for the unmodified version. Crucially, the hardest problems gave the greatest improvements. The pruning technique based on parameter domains often gave speedups by an order of magnitude or more for difficult problems, both with the default UCPOP search strategy and with our improved strategy. The Lisp code for our techniques and for the test problems is provided in on-line appendices.

Connected through hyperedge HE-1 (induced by source paper "planning through stochastic local search and temporal action graphs in lpg"):
  with members:
    - "pddl2 1 an extension to pddl for expressing temporal planning domains"
    - "the metric ff planning system translating ignoring delete lists to numeric state variables"
    - "the 3rd international planning competition results and analysis"
    - "the ff planning system fast plan generation through heuristic search"
    - "efficient implementation of the plan graph in stan"
    - "the automatic inference of state invariants in tim"
    - "flaw selection strategies for partial order planning"

Connected through hyperedge HE-2 (induced by source paper "vhpop versatile heuristic partial order planner"):
  with members:
    - "the ff planning system fast plan generation through heuristic search"
    - "the grt planning system backward heuristic construction in forward state space planning"
    - "flaw selection strategies for partial order planning"

Connected through hyperedge HE-3 (induced by source paper "a critical assessment of benchmark comparison in planning"):
  with members:
    - "pddl2 1 an extension to pddl for expressing temporal planning domains"
    - "the ff planning system fast plan generation through heuristic search"
    - "efficient implementation of the plan graph in stan"
    - "flaw selection strategies for partial order planning"

Connected through hyperedge HE-4 (induced by source paper "the automatic inference of state invariants in tim"):
  with members:
    - "efficient implementation of the plan graph in stan"

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)