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 with polynomial ordinary differential equations"
  Abstract: In 1941, Claude Shannon introduced the General Purpose Analog Computer (GPAC) as a mathematical model of Differential Analysers, that is to say as a model of continuous-time analog (mechanical, and later on electronic) machines of that time.Following Shannon's arguments, functions generated by the GPAC must satisfy a polynomial differential algebraic equation (DAE). As it is known that some computable functions like Euler's ź ( x ) = ź 0 ∞ t x - 1 e - t d t or Riemann's Zeta function ź ( x ) = ź k = 0 ∞ 1 k x do not satisfy any polynomial DAE, this argument has often been used to demonstrate in the past that the GPAC is less powerful than digital computation.It was proved in Bournez etźal. (2007), that if a more modern notion of computation is considered, i.e.źin particular if computability is not restricted to real-time generation of functions, the GPAC is actually equivalent to Turing machines.Our purpose is first to discuss the robustness of the notion of computation involved in Bournez etźal. (2007), by establishing that many natural variants of the notion of computation from this paper lead to the same computability result.Second, to go from these computability results towards considerations about (time) complexity: we explore several natural variants for measuring time/space complexity of a computation.Quite surprisingly, whereas defining a robust time complexity for general continuous time systems is a well known open problem, we prove that all variants are actually equivalent even at the complexity level. As a consequence, it seems that a robust and well defined notion of time complexity exists for the GPAC, or equivalently for computations by polynomial ordinary differential equations.Another side effect of our proof is also that we show in some way that polynomial ordinary differential equations can actually be used as a kind of programming model, and that there is a rather nice and robust notion of ordinary differential equation (ODE) programming.

Connected through hyperedge HE-1 (induced by source paper "polynomial time corresponds to solutions of polynomial ordinary differential equations of polynomial length"):
  with members:
    - "computational complexity of solving polynomial differential equations over unbounded domains"
    - "computational complexity of solving polynomial differential equations over unbounded domains with non rational coefficients"
    - "on the functions generated by the general purpose analog computer"
    - "on the complexity of solving initial value problems"
    - "lipschitz continuous ordinary differential equations are polynomial space complete"
    - "probabilistic analysis of a differential equation for linear programming"

Connected through hyperedge HE-2 (induced by source paper "on the functions generated by the general purpose analog computer"):
  with members:
    - "on the complexity of solving initial value problems"

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.CC(Computational Complexity)