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: "macro ff improving ai planning with automatically learned macro operators"
  Abstract: Despite recent progress in AI planning, many benchmarks remain challenging for current planners. In many domains, the performance of a planner can greatly be improved by discovering and exploiting information about the domain structure that is not explicitly encoded in the initial PDDL formulation. In this paper we present and compare two automated methods that learn relevant information from previous experience in a domain and use it to solve new problem instances. Our methods share a common four-step strategy. First, a domain is analyzed and structural information is extracted, then macro-operators are generated based on the previously discovered structure. A filtering and ranking procedure selects the most useful macro-operators. Finally, the selected macros are used to speed up future searches.#R##N##R##N#We have successfully used such an approach in the fourth international planning competition IPC-4. Our system, Macro-FF, extends Hoffmann's state-of-the-art planner FF 2.3 with support for two kinds of macro-operators, and with engineering enhancements. We demonstrate the effectiveness of our ideas on benchmarks from international planning competitions. Our results indicate a large reduction in search effort in those complex domains where structural information can be inferred.

Connected through hyperedge HE-1 (induced by source paper "construction of macro actions for deep reinforcement learning"):
  with members:
    - "macro action reinforcement learning with sequence disentanglement using variational autoencoder"
    - "meta learning shared hierarchies"
    - "learning to repeat fine grained action repetition for deep reinforcement learning"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "curiosity driven exploration by self supervised prediction"
    - "stochastic neural networks for hierarchical reinforcement learning"
    - "proximal policy optimization algorithms"
    - "learning and transfer of modulated locomotor controllers"

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"
    - "marvin a heuristic search planner with online macro action learning"
    - "the fast downward planning system"

Connected through hyperedge HE-3 (induced by source paper "mining useful macro actions in planning"):
  with members:
    - "marvin a heuristic search planner with online macro action learning"
    - "the 3rd international planning competition results and analysis"
    - "the ff planning system fast plan generation through heuristic search"

Connected through hyperedge HE-4 (induced by source paper "optimal temporal logic planning in probabilistic semantic maps"):
  with members:
    - "probably approximately correct mdp learning and control with temporal logic constraints"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-5 (induced by source paper "evaluation of a simple scalable parallel best first search strategy"):
  with members:
    - "best first heuristic search for multicore machines"
    - "adaptive parallel iterative deepening search"

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"
    - "marvin a heuristic search planner with online macro action learning"
    - "the fast downward planning system"
    - "the ff planning system fast plan generation through heuristic search"

Connected through hyperedge HE-7 (induced by source paper "proximity based non uniform abstractions for approximate planning"):
  with members:
    - "continuous value function approximation for sequential bidding policies"
    - "hierarchical solution of markov decision processes using macro actions"
    - "correlated action effects in decision theoretic regression"
    - "spudd stochastic planning using decision diagrams"
    - "efficient solution algorithms for factored mdps"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

Connected through hyperedge HE-8 (induced by source paper "scaling up heuristic planning with relational decision trees"):
  with members:
    - "the lama planner guiding cost based anytime planning with landmarks"
    - "marvin a heuristic search planner with online macro action learning"
    - "ordered landmarks in planning"
    - "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)