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 "marvin a heuristic search planner with online macro action learning"):
  Abstract: This paper describes Marvin, a planner that competed in the Fourth International Planning Competition (IPC 4). Marvin uses action-sequence-memoisation techniques to generate macro-actions, which are then used during search for a solution plan. We provide an overview of its architecture and search behaviour, detailing the algorithms used. We also empirically demonstrate the effectiveness of its features in various planning domains; in particular, the effects on performance due to the use of macro-actions, the novel features of its search behaviour, and the native support of ADL and Derived Predicates.

Includes members:
  Member "the fast downward planning system":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transferable task execution from pixels through deep planning domain learning")
  Member "where ignoring delete lists works local search topology in planning benchmarks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to rank for synthesizing planning heuristics")
  Member "macro ff improving ai planning with automatically learned macro operators":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "construction of macro actions for deep reinforcement learning")
  Member "the deterministic part of ipc 4 an overview":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "handling pddl3 0 state trajectory constraints with temporal landmarks")
  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 "the ff planning system fast plan generation through heuristic search":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "what should i do now marrying reinforcement learning and symbolic planning")
  Member "efficient implementation of the plan graph in stan":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "planning through stochastic local search and temporal action graphs in lpg")

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)