human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "where ignoring delete lists works local search topology in planning benchmarks"):
  Abstract: Between 1998 and 2004, the planning community has seen vast progress in terms of the sizes of benchmark examples that domain-independent planners can tackle successfully. The key technique behind this progress is the use of heuristic functions based on relaxing the planning task at hand, where the relaxation is to assume that all delete lists are empty. The unprecedented success of such methods, in many commonly used benchmark examples, calls for an understanding of what classes of domains these methods are well suited for. In the investigation at hand, we derive a formal background to such an understanding. We perform a case study covering a range of 30 commonly used STRIPS and ADL benchmark domains, including all examples used in the first four international planning competitions. We prove connections between domain structure and local search topology – heuristic cost surface properties – under an idealized version of the heuristic functions used in modern planners. The idealized heuristic function is called h + , and differs from the practically used functions in that it returns the length of an optimal relaxed plan, which is NP-hard to compute. We identify several key characteristics of the topology under h + , concerning the existence/non-existence of unrecognized dead ends, as well as the existence/non-existence of constant upper bounds on the difficulty of escaping local minima and benches. These distinctions divide the (set of all) planning domains into a taxonomy of classes of varying h + topology. As it turns out, many of the 30 investigated domains lie in classes with a relatively easy topology. Most particularly, 12 of the domains lie in classes where FF’s search algorithm, provided with h + , is a polynomial solving mechanism. We also present results relating h + to its approximation as implemented in FF. The behavior regarding dead ends is provably the same. We summarize the results of an empirical investigation showing that, in many domains, the topological qualities of h + are largely inherited by the approximation. The overall investigation gives a rare example of a successful analysis of the connections between typical-case problem structure, and search performance. The theoretical investigation also gives hints on how the topological phenomena might be automatically recognizable by domain analysis techniques. We outline some preliminary steps we made into that direction.

Includes members:
  Member "engineering benchmarks for planning the domains used in the deterministic part of ipc 4":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "structure and problem hardness goal asymmetry and dpll proofs in sat based planning")
  Member "the deterministic part of ipc 4 an overview":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "handling pddl3 0 state trajectory constraints with temporal landmarks")
  Member "taming numbers and durations in the model checking integrated planning system":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a hybrid lp rpg heuristic for modelling numeric resource flows in planning")
  Member "the metric ff planning system translating ignoring delete lists to numeric state variables":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "what should i do now marrying reinforcement learning and symbolic planning")
  Member "the 3rd international planning competition results and analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning domain independent planning heuristics with hypergraph networks")
  Member "planning through stochastic local search and temporal action graphs in lpg":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "automated algorithm selection survey and perspectives")
  Member "the ff planning system fast plan generation through heuristic search":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "what should i do now marrying reinforcement learning and symbolic planning")
  Member "the grt planning system backward heuristic construction in forward state space planning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sapa a multi objective metric temporal planner")

Question: Please tell me which class the source paper 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)