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 "engineering benchmarks for planning the domains used in the deterministic part of ipc 4"):
  Abstract: In a field of research about general reasoning mechanisms, it is essential to have appropriate benchmarks. Ideally, the benchmarks should reflect possible applications of the developed technology. In AI Planning, researchers more and more tend to draw their testing examples from the benchmark collections used in the International Planning Competition (IPC). In the organization of (the deterministic part of) the fourth IPC, IPC-4, the authors therefore invested significant effort to create a useful set of benchmarks. They come from five different (potential) real-world applications of planning: airport ground traffic control, oil derivative transportation in pipeline networks, model-checking safety properties, power supply restoration, and UMTS call setup. Adapting and preparing such an application for use as a benchmark in the IPC involves, at the time, inevitable (often drastic) simplifications, as well as careful choice between, and engineering of, domain encodings. For the first time in the IPC, we used compilations to formulate complex domain features in simple languages such as STRIPS, rather than just dropping the more interesting problem constraints in the simpler language subsets. The article explains and discusses the five application domains and their adaptation to form the PDDL test suites used in IPC-4. We summarize known theoretical results on structural properties of the domains, regarding their computational complexity and provable properties of their topology under the h+ function (an idealized version of the relaxed plan heuristic). We present new (empirical) results illuminating properties such as the quality of the most wide-spread heuristic functions (planning graph, serial planning graph, and relaxed plan), the growth of propositional representations over instance size, and the number of actions available to achieve each fact; we discuss these data in conjunction with the best results achieved by the different kinds of planners participating in IPC-4.

Includes members:
  Member "supply restoration in power distribution systems a case study in integrating model based diagnosis and repair planning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the deterministic part of ipc 4 an overview")
  Member "the fast downward planning system":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transferable task execution from pixels through deep planning domain learning")
  Member "an approach to temporal planning and scheduling in domains with predictable exogenous events":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards explainable ai planning as a service")
  Member "where ignoring delete lists works local search topology in planning benchmarks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to rank for synthesizing planning heuristics")
  Member "the deterministic part of ipc 4 an overview":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "a hybrid lp rpg heuristic for modelling numeric resource flows in planning")
  Member "pddl2 1 an extension to pddl for expressing temporal planning domains":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transferable task execution from pixels through deep planning domain learning")
  Member "the metric ff planning system translating ignoring delete lists to numeric state variables":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "what should i do now marrying reinforcement learning and symbolic planning")

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)