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: "computing the least fixed point of positive polynomial systems"
  Abstract: We consider equation systems of the form X_1 = f_1(X_1, ..., X_n), ..., X_n = f_n(X_1, ..., X_n) where f_1, ..., f_n are polynomials with positive real coefficients. In vector form we denote such an equation system by X = f(X) and call f a system of positive polynomials, short SPP. Equation systems of this kind appear naturally in the analysis of stochastic models like stochastic context-free grammars (with numerous applications to natural language processing and computational biology), probabilistic programs with procedures, web-surfing models with back buttons, and branching processes. The least nonnegative solution mu f of an SPP equation X = f(X) is of central interest for these models. Etessami and Yannakakis have suggested a particular version of Newton's method to approximate mu f. #R##N#We extend a result of Etessami and Yannakakis and show that Newton's method starting at 0 always converges to mu f. We obtain lower bounds on the convergence speed of the method. For so-called strongly connected SPPs we prove the existence of a threshold k_f such that for every i >= 0 the (k_f+i)-th iteration of Newton's method has at least i valid bits of mu f. The proof yields an explicit bound for k_f depending only on syntactic parameters of f. We further show that for arbitrary SPP equations Newton's method still converges linearly: there are k_f>=0 and alpha_f>0 such that for every i>=0 the (k_f+alpha_f i)-th iteration of Newton's method has at least i valid bits of mu f. The proof yields an explicit bound for alpha_f; the bound is exponential in the number of equations, but we also show that it is essentially optimal. Constructing a bound for k_f is still an open problem. Finally, we also provide a geometric interpretation of Newton's method for SPPs.

Connected through hyperedge HE-1 (induced by source paper "stochastic context free grammars regular languages and newton s method"):
  with members:
    - "upper bounds for newton s method on monotone polynomial systems and p time model checking of probabilistic one counter automata"
    - "polynomial time algorithms for multi type branching processes and stochastic context free grammars"
    - "polynomial time algorithms for branching markov decision processes and probabilistic min max polynomial bellman equations"
    - "computing least fixed points of probabilistic systems of polynomials"
    - "linear encodings of bounded ltl model checking"
    - "model checking probabilistic pushdown automata"

Connected through hyperedge HE-2 (induced by source paper "upper bounds for newton s method on monotone polynomial systems and p time model checking of probabilistic one counter automata"):
  with members:
    - "exact algorithms for solving stochastic games"
    - "polynomial time algorithms for multi type branching processes and stochastic context free grammars"
    - "efficient analysis of probabilistic programs with an unbounded counter"
    - "linear encodings of bounded ltl model checking"
    - "model checking probabilistic pushdown automata"

Connected through hyperedge HE-3 (induced by source paper "polynomial time algorithms for branching markov decision processes and probabilistic min max polynomial bellman equations"):
  with members:
    - "polynomial time algorithms for multi type branching processes and stochastic context free grammars"
    - "approximating the termination value of one counter mdps and stochastic games"
    - "linear encodings of bounded ltl model checking"
    - "model checking probabilistic pushdown automata"

Connected through hyperedge HE-4 (induced by source paper "model checking stochastic branching processes"):
  with members:
    - "polynomial time algorithms for multi type branching processes and stochastic context free grammars"
    - "on probabilistic parallel programs with process creation and synchronisation"
    - "computing least fixed points of probabilistic systems of polynomials"

Connected through hyperedge HE-5 (induced by source paper "on probabilistic parallel programs with process creation and synchronisation"):
  with members:
    - "runtime analysis of probabilistic programs with unbounded recursion"
    - "recursive concurrent stochastic games"

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.NA(Numerical Analysis)