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: "partial observation stochastic games how to win when belief fails"
  Abstract: In two-player finite-state stochastic games of partial observation on graphs, in every state of the graph, the players simultaneously choose an action, and their joint actions determine a probability distribution over the successor states. We consider reachability objectives where player 1 tries to ensure a target state to be visited almost-surely or positively. On the basis of information, the game can be one-sided with either (a)player 1 or (b)player 2 having partial observation, or two-sided with both players having partial observation. On the basis of randomization (a)players may not be allowed to use randomization (pure strategies), or (b)may choose a probability distribution over actions but the actual random choice is not visible (actions invisible), or (c)may use full randomization. Our results for pure strategies are as follows: (1)For one-sided games with player 2 perfect observation we show that belief-based strategies are not sufficient, and present an exponential upper bound on memory both for almost-sure and positive winning strategies; we show that the problem of deciding the existence of almost-sure and positive winning strategies for player 1 is EXPTIME-complete and present symbolic algorithms that avoid the explicit exponential construction. (2)For one-sided games with player 1 perfect observation we show that non-elementary memory is both necessary and sufficient for both almost-sure and positive winning strategies. (3)We show that for the two-sided case finite memory strategies are sufficient for both positive and almost-sure winning. We establish the equivalence of the almost-sure winning problem for pure strategies with randomized strategies with actions invisible. Our equivalence result exhibit serious flaws in previous results in the literature: we show a non-elementary memory lower bound for almost-sure winning whereas an exponential upper bound was claimed.

Connected through hyperedge HE-1 (induced by source paper "alternating tree automata with qualitative semantics"):
  with members:
    - "monadic second order logic with path measure quantifier is undecidable"
    - "alternating nonzero automata"
    - "emptiness of zero automata is decidable"
    - "monadic second order logic with measure and category quantifiers"
    - "pure strategies in imperfect information stochastic games"
    - "qualitative concurrent stochastic games with imperfect information"
    - "algorithms for omega regular games with imperfect information"

Connected through hyperedge HE-2 (induced by source paper "strategy logic with imperfect information"):
  with members:
    - "decidability results for atl with imperfect information and perfect recall"
    - "games on graphs with a public signal monitoring"
    - "the fixed initial credit problem for partial observation energy games is ack complete"
    - "hierarchical information and the synthesis of distributed strategies"
    - "hierarchical information patterns and distributed strategy synthesis"
    - "pure nash equilibria in concurrent deterministic games"
    - "atlsc with partial observation"
    - "games with a weak adversary"

Connected through hyperedge HE-3 (induced by source paper "motion planning under partial observability using game based abstraction"):
  with members:
    - "counterexample guided abstraction refinement for pomdps"
    - "a storm is coming a modern probabilistic model checker"
    - "optimal cost almost sure reachability in pomdps"
    - "qualitative analysis of pomdps with temporal logic specifications for robotics applications"
    - "discretized approximations for pomdp with average cost"
    - "control of probabilistic systems under dynamic partially known environments with temporal logic specifications"

Connected through hyperedge HE-4 (induced by source paper "deciding maxmin reachability in half blind stochastic games"):
  with members:
    - "profinite techniques for probabilistic automata and the optimality of the markov monoid algorithm"
    - "deciding the value 1 problem for probabilistic leaktight automata"
    - "the complexity of partial observation stochastic parity games with finite memory strategies"
    - "regular cost functions part i logic and algebra over words"
    - "decidable problems for probabilistic automata on infinite words"
    - "power of randomization in automata on infinite strings"

Connected through hyperedge HE-5 (induced by source paper "dynamic programming for one sided partially observable pursuit evasion games"):
  with members:
    - "point based pomdp algorithms improved analysis and implementation"

Connected through hyperedge HE-6 (induced by source paper "stochastic equilibria under imprecise deviations in terminal reward concurrent games"):
  with members:
    - "the complexity of nash equilibria in limit average games"

Connected through hyperedge HE-7 (induced by source paper "pure strategies in imperfect information stochastic games"):
  with members:
    - "qualitative concurrent stochastic games with imperfect information"
    - "algorithms for omega regular games with imperfect information"

Connected through hyperedge HE-8 (induced by source paper "verification and control of partially observable probabilistic real time systems"):
  with members:
    - "what is decidable about partially observable markov decision processes with omega regular objectives"

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.GT(Computer Science and Game Theory)