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: "the ff planning system fast plan generation through heuristic search"
  Abstract: We describe and evaluate the algorithmic techniques that are used in the FF planning system. Like the HSP system, FF relies on forward state space search, using a heuristic that estimates goal distances by ignoring delete lists. Unlike HSP's heuristic, our method does not assume facts to be independent. We introduce a novel search strategy that combines hill-climbing with systematic search, and we show how other powerful heuristic information can be extracted and used to prune the search space. FF was the most successful automatic planner at the recent AIPS-2000 planning competition. We review the results of the competition, give data for other benchmark domains, and investigate the reasons for the runtime performance of FF compared to HSP.

Connected through hyperedge HE-1 (induced by source paper "what should i do now marrying reinforcement learning and symbolic planning"):
  with members:
    - "hyp despot a hybrid parallel algorithm for online planning under uncertainty"
    - "fusion volumetric object level slam"
    - "gibson env real world perception for embodied agents"
    - "chalet cornell house agent learning environment"
    - "on evaluation of embodied navigation agents"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "shifting the baseline single modality performance on visual navigation qa"
    - "neural modular control for embodied question answering"

Connected through hyperedge HE-2 (induced by source paper "unsupervised grounding of plannable first order logic representation from images"):
  with members:
    - "photo realistic blocksworld dataset"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "deepproblog neural probabilistic logic programming"
    - "relational deep reinforcement learning"
    - "logical rule induction and theory learning using neural theorem proving"
    - "relational inductive biases deep learning and graph networks"
    - "a simple neural network module for relational reasoning"
    - "end to end differentiable proving"

Connected through hyperedge HE-3 (induced by source paper "neural symbolic descriptive action model from images the search for strips"):
  with members:
    - "unsupervised grounding of plannable first order logic representation from images"
    - "learning action models from disordered and noisy plan traces"
    - "towards stable symbol grounding with zero suppressed state autoencoder"
    - "learning strips action models with classical planning"
    - "learning plannable representations with causal infogan"
    - "learning finite state representations of recurrent policy networks"
    - "extracting action sequences from texts based on deep reinforcement learning"
    - "dvae discrete variational autoencoders with overlapping transformations"

Connected through hyperedge HE-4 (induced by source paper "landmark based approaches for goal recognition as planning"):
  with members:
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "a unified framework for planning in adversarial and cooperative environments"
    - "heuristic online goal recognition in continuous domains"
    - "session analysis using plan recognition"
    - "landmark based plan recognition"
    - "accounting for context in plan recognition with application to traffic monitoring"
    - "the lama planner guiding cost based anytime planning with landmarks"
    - "the fast downward planning system"

Connected through hyperedge HE-5 (induced by source paper "real time tree search with pessimistic scenarios"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "continual match based training in pommerman technical report"
    - "backplay man muss immer umkehren"
    - "multi agent reinforcement learning a report on challenges and approaches"
    - "pommerman a multi agent playground"
    - "how to combine tree search methods in reinforcement learning"
    - "feedback based tree search for reinforcement learning"
    - "thinking fast and slow with deep learning and tree search"

Connected through hyperedge HE-6 (induced by source paper "timeline based planning expressiveness and complexity"):
  with members:
    - "a new rule for ltl tableaux"
    - "sat based explicit ltl reasoning"
    - "an on the fly tableau based decision procedure for pdl satisfiability"
    - "modelling mixed discrete continuous domains for planning"
    - "pddl2 1 an extension to pddl for expressing temporal planning domains"
    - "clausal temporal resolution"
    - "the computational complexity of probabilistic planning"

Connected through hyperedge HE-7 (induced by source paper "hybrid planning for dynamic multimodal stochastic shortest paths"):
  with members:
    - "dynamic real time multimodal routing with hierarchical hybrid planning"
    - "an anytime algorithm for task and motion mdps"
    - "benchmarking deep reinforcement learning for continuous control"
    - "hierarchical solution of markov decision processes using macro actions"
    - "solving factored mdps with hybrid state and action variables"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

Connected through hyperedge HE-8 (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"
    - "marvin a heuristic search planner with online macro action learning"
    - "the fast downward planning system"

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)