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: "additive pattern database heuristics"
  Abstract: We explore a method for computing admissible heuristic evaluation functions for search problems. It utilizes pattern databases (Culberson & Schaeffer, 1998), which are precomputed tables of the exact cost of solving various subproblems of an existing problem. Unlike standard pattern database heuristics, however, we partition our problems into disjoint sub-problems, so that the costs of solving the different subproblems can be added together without overestimating the cost of solving the original problem. Previously (Korf & Felner, 2002) we showed how to statically partition the sliding-tile puzzles into disjoint groups of tiles to compute an admissible heuristic, using the same partition for each state and problem instance. Here we extend the method and show that it applies to other domains as well. We also present another method for additive heuristics which we call dynamically partitioned pattern databases. Here we partition the problem into disjoint subproblems for each state of the search dynamically. We discuss the pros and cons of each of these methods and apply both methods to three different problem domains: the sliding-tile puzzles, the 4-peg Towers of Hanoi problem, and finding an optimal vertex cover of a graph. We find that in some problem domains, static partitioning is most effective. while in others dynamic partitioning is a better choice. In each of these problem domains, either statically partitioned or dynamically partitioned pattern database heuristics are the best known heuristics for the problem.

Connected through hyperedge HE-1 (induced by source paper "adaptively informed trees ait fast asymptotically optimal path planning through adaptive heuristics"):
  with members:
    - "batch informed trees bit informed asymptotically optimal anytime search"
    - "sampling based optimal kinodynamic planning with motion primitives"
    - "lazy receding horizon a for efficient path planning in graphs with expensive to evaluate edges"
    - "informed sampling for asymptotically optimal path planning"
    - "a unifying formalism for shortest path problems with expensive edge evaluations via lazy best first search over paths with edge selectors"
    - "collision detection or nearest neighbor search on the computational bottleneck in sampling based motion planning"
    - "deterministic sampling based motion planning optimality complexity and performance"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"

Connected through hyperedge HE-2 (induced by source paper "procedural generation of initial states of sokoban"):
  with members:
    - "single agent policy tree search with guarantees"
    - "imagination augmented agents for deep reinforcement learning"
    - "the ff planning system fast plan generation through heuristic search"

Connected through hyperedge HE-3 (induced by source paper "exact structure learning of bayesian networks by optimal path extension"):
  with members:
    - "a tutorial on learning with bayesian networks"

Connected through hyperedge HE-4 (induced by source paper "an improved admissible heuristic for learning optimal bayesian networks"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "a branch and bound algorithm for mdl learning bayesian networks"
    - "improving the scalability of optimal bayesian network learning with external memory frontier breadth first branch and bound search"
    - "bayesian network learning with cutting planes"
    - "exact structure discovery in bayesian networks with less space"
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "ordering based search a simple and effective algorithm for learning bayesian networks"

Connected through hyperedge HE-5 (induced by source paper "implicit abstraction heuristics"):
  with members:
    - "a general theory of additive state space abstractions"
    - "the fast downward planning system"

Connected through hyperedge HE-6 (induced by source paper "improving heuristics through relaxed search an analysis of tp4 and hsp a in the 2004 planning competition"):
  with members:
    - "the deterministic part of ipc 4 an overview"
    - "pddl2 1 an extension to pddl for expressing temporal planning domains"

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)