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: "pomdps under probabilistic semantics"
  Abstract: We consider partially observable Markov decision processes (POMDPs) with limit-average payoff, where a reward value in the interval [0,1] is associated to every transition, and the payoff of an infinite path is the long-run average of the rewards. We consider two types of path constraints: (i) quantitative constraint defines the set of paths where the payoff is at least a given threshold {\lambda} in (0, 1]; and (ii) qualitative constraint which is a special case of quantitative constraint with {\lambda} = 1. We consider the computation of the almost-sure winning set, where the controller needs to ensure that the path constraint is satisfied with probability 1. Our main results for qualitative path constraint are as follows: (i) the problem of deciding the existence of a finite-memory controller is EXPTIME-complete; and (ii) the problem of deciding the existence of an infinite-memory controller is undecidable. For quantitative path constraint we show that the problem of deciding the existence of a finite-memory controller is undecidable.

Connected through hyperedge HE-1 (induced by source paper "parameter independent strategies for pmdps via pomdps"):
  with members:
    - "sea param exploring schedulers in parametric mdps"
    - "sequential convex programming for the efficient verification of parametric mdps"
    - "automated experiment design for data efficient verification of parametric markov decision processes"
    - "a storm is coming a modern probabilistic model checker"
    - "ares adaptive receding horizon synthesis of optimal plans"
    - "parameter synthesis for markov models faster than ever"
    - "a symbolic sat based algorithm for almost sure reachability with small strategies in pomdps"
    - "optimal cost almost sure reachability in pomdps"

Connected through hyperedge HE-2 (induced by source paper "on avoidance learning with partial observability"):
  with members:
    - "finding approximate pomdp solutions through belief compression"
    - "nonapproximability results for partially observable markov decision processes"

Connected through hyperedge HE-3 (induced by source paper "further improvements of determinization methods for fuzzy finite automata"):
  with members:
    - "supervisory control of fuzzy discrete event systems for simulation equivalence"
    - "brzozowski type determinization for fuzzy automata"
    - "determinization of fuzzy automata by means of the degrees of language inclusion"
    - "a possibilistic model for qualitative sequential decision problems under uncertainty in partially observable environments"
    - "qualitative possibilistic mixed observable mdps"
    - "decidability of minimization of fuzzy automata"
    - "computation of the greatest simulations and bisimulations between fuzzy automata"
    - "bisimulations for fuzzy automata"

Connected through hyperedge HE-4 (induced by source paper "what is decidable about partially observable markov decision processes with omega regular objectives"):
  with members:
    - "randomness for free"
    - "qualitative analysis of partially observable markov decision processes"
    - "algorithms for omega regular games with imperfect information"
    - "reinforcement learning a survey"

Connected through hyperedge HE-5 (induced by source paper "randomness for free"):
  with members:
    - "what is decidable about partially observable markov decision processes with omega regular objectives"
    - "partial observation stochastic games how to win when belief fails"
    - "qualitative concurrent stochastic games with imperfect information"
    - "algorithms for omega regular games with imperfect information"
    - "interactive small step algorithms ii abstract state machines and the characterization theorem"

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)