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: "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.

Connected through hyperedge HE-1 (induced by source paper "learning neural search policies for classical planning"):
  with members:
    - "learning classical planning strategies with policy gradient"
    - "improving exploration in evolution strategies for deep reinforcement learning via a population of novelty seeking agents"
    - "learning to rank for synthesizing planning heuristics"
    - "the lama planner guiding cost based anytime planning with landmarks"
    - "the fast downward planning system"
    - "the ff planning system fast plan generation through heuristic search"

Connected through hyperedge HE-2 (induced by source paper "generalized planning with procedural domain control knowledge"):
  with members:
    - "scaling up heuristic planning with relational decision trees"
    - "the lama planner guiding cost based anytime planning with landmarks"
    - "compiling uncertainty away in conformant planning problems with bounded width"
    - "the fast downward planning system"
    - "macro ff improving ai planning with automatically learned macro operators"

Connected through hyperedge HE-3 (induced by source paper "learning classical planning strategies with policy gradient"):
  with members:
    - "learning to rank for synthesizing planning heuristics"
    - "the lama planner guiding cost based anytime planning with landmarks"
    - "the fast downward planning system"
    - "the ff planning system fast plan generation through heuristic search"

Connected through hyperedge HE-4 (induced by source paper "mining useful macro actions in planning"):
  with members:
    - "macro ff improving ai planning with automatically learned macro operators"
    - "the 3rd international planning competition results and analysis"
    - "the ff planning system fast plan generation through heuristic search"

Connected through hyperedge HE-5 (induced by source paper "axioms in model based planners"):
  with members:
    - "loosely coupled formulations for automated planning an integer programming perspective"
    - "the fast downward planning system"
    - "optiplan unifying ip based and graph based planning"

Connected through hyperedge HE-6 (induced by source paper "online speedup learning for optimal planning"):
  with members:
    - "implicit abstraction heuristics"
    - "compiling uncertainty away in conformant planning problems with bounded width"
    - "soft goals can be compiled away"
    - "the fast downward planning system"
    - "macro ff improving ai planning with automatically learned macro operators"
    - "the ff planning system fast plan generation through heuristic search"

Connected through hyperedge HE-7 (induced by source paper "scaling up heuristic planning with relational decision trees"):
  with members:
    - "the lama planner guiding cost based anytime planning with landmarks"
    - "macro ff improving ai planning with automatically learned macro operators"
    - "ordered landmarks in planning"
    - "the ff planning system fast plan generation through heuristic search"

Connected through hyperedge HE-8 (induced by source paper "on the fly macros"):
  with members:
    - "the fast downward planning system"
    - "macro ff improving ai planning with automatically learned macro operators"
    - "structure and complexity in planning with unary operators"
    - "the ff planning system fast plan generation through heuristic search"

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)