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: "replanning in domains with partial information and sensing actions"
  Abstract: Replanning via determinization is a recent, popular approach for online planning in MDPs. In this paper we adapt this idea to classical, non-stochastic domains with partial information and sensing actions, presenting a new planner: SDR (Sample, Determinize, Replan). At each step we generate a solution plan to a classical planning problem induced by the original problem. We execute this plan as long as it is safe to do so. When this is no longer the case, we replan. The classical planning problem we generate is based on the translation-based approach for conformant planning introduced by Palacios and Geffner. The state of the classical planning problem generated in this approach captures the belief state of the agent in the original problem. Unfortunately, when this method is applied to planning problems with sensing, it yields a non-deterministic planning problem that is typically very large. Our main contribution is the introduction of state sampling techniques for overcoming these two problems. In addition, we introduce a novel, lazy, regressionbased method for querying the agent's belief state during run-time. We provide a comprehensive experimental evaluation of the planner, showing that it scales better than the state-of-the-art CLG planner on existing benchmark problems, but also highlighting its weaknesses with new domains. We also discuss its theoretical guarantees.

Connected through hyperedge HE-1 (induced by source paper "supervisor synthesis of pomdp based on automata learning"):
  with members:
    - "counterexample guided abstraction refinement for pomdps"
    - "a storm is coming a modern probabilistic model checker"
    - "permissive controller synthesis for probabilistic systems"
    - "a symbolic sat based algorithm for almost sure reachability with small strategies in pomdps"
    - "randomness for free"
    - "speeding up the convergence of value iteration in partially observable markov decision processes"

Connected through hyperedge HE-2 (induced by source paper "hybrid conditional planning using answer set programming"):
  with members:
    - "clingo asp control preliminary report"
    - "reasoning and planning with sensing actions incomplete information and static causal laws using answer set programming"
    - "planning graph heuristics for belief space search"

Connected through hyperedge HE-3 (induced by source paper "integrating active sensing into reactive synthesis with temporal logic constraints under partial observations"):
  with members:
    - "abstractions and sensor design in partial information reactive controller synthesis"
    - "control of probabilistic systems under dynamic partially known environments with temporal logic specifications"
    - "partial observation stochastic games how to win when belief fails"
    - "algorithms for omega regular games with imperfect information"
    - "decisive markov chains"

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)