human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "greatest fixed points of probabilistic min max polynomial equations and reachability for branching markov decision processes"):
  Abstract: We give polynomial time algorithms for quantitative (and qualitative) reachability analysis for Branching Markov Decision Processes (BMDPs). Specifically, given a BMDP, and given an initial population, where the objective of the controller is to maximize (or minimize) the probability of eventually reaching a population that contains an object of a desired (or undesired) type, we give algorithms for approximating the supremum (infimum) reachability probability, within desired precision ǫ > 0, in time polynomial in the encoding size of the BMDP and in log(1/ǫ). We furthermore give P-time algorithms for computing ǫ-optimal strategies for both maximization and minimization of reachability probabilities. We also give P-time algorithms for all associated qualitative analysis problems, namely: deciding whether the optimal (supremum or infimum) reachability probabilities are 0 or 1. Prior to this paper, approximation of optimal reachability probabilities for BMDPs was not even known to be decidable. Our algorithms exploit the following basic fact: we show that for any BMDP, its maximum (minimum) non-reachability probabilities are given by the greatest fixed point (GFP) solution g∗ ∈ [0, 1]n of a corresponding monotone max (min) Probabilistic Polynomial System of equations (max/minPPS), x = P (x), which are the Bellman optimality equations for a BMDP with non-reachability objectives. We show how to compute the GFP of max/minPPSs to desired precision in P-time. We also study more general branching simple stochastic games (BSSGs) with (non-)reachability objectives. We show that: (1) the value of these games is captured by the GFP, g∗, of a corresponding max-minPPS, x = P (x); (2) the quantitative problem of approximating the value is in TFNP; and (3) the qualitative problems associated with the value are all solvable in P-time.

Includes members:
  Member "analysis of probabilistic basic parallel processes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reachability for branching concurrent stochastic games")
  Member "polynomial time algorithms for multi type branching processes and stochastic context free grammars":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "zero reachability in probabilistic multi counter automata")
  Member "model checking stochastic branching processes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the uniform measure of simple regular sets of infinite trees")
  Member "polynomial time algorithms for branching markov decision processes and probabilistic min max polynomial bellman equations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reachability for branching concurrent stochastic games")
  Member "qualitative reachability in stochastic bpa games":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reachability for branching concurrent stochastic games")
  Member "linear encodings of bounded ltl model checking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an abstraction free method for multi robot temporal logic optimal control synthesis")
  Member "model checking probabilistic pushdown automata":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deciding fast termination for probabilistic vass with nondeterminism")

Question: Please tell me which class the source paper 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.CC(Computational Complexity)