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: "computational complexity of solving polynomial differential equations over unbounded domains"
  Abstract: In this paper we investigate the computational complexity of solving ordinary differential equations (ODEs) y ' = p ( y ) over unbounded time domains, where p is a vector of polynomials. Contrarily to the bounded (compact) time case, this problem has not been well-studied, apparently due to the "intuition" that it can always be reduced to the bounded case by using rescaling techniques. However, as we show in this paper, rescaling techniques do not seem to provide meaningful insights on the complexity of this problem, since the use of such techniques introduces a dependence on parameters which are hard to compute.We present algorithms which numerically solve these ODEs over unbounded time domains. These algorithms have guaranteed accuracy, i.e. given some arbitrarily large time t and error bound e as input, they will output a value y ? which satisfies ? y ( t ) - y ? ? ? e . We analyze the complexity of these algorithms and show that they compute y ? in time polynomial in several quantities including the time t, the accuracy of the output e and the length of the curve y from 0 to t, assuming it exists until time t. We consider both algebraic complexity and bit complexity.

Connected through hyperedge HE-1 (induced by source paper "parametrised second order complexity theory with applications to the study of interval computation"):
  with members:
    - "bounded time computation on metric spaces and banach spaces"
    - "polynomial running times for polynomial time oracle machines"
    - "complexity theory for spaces of integrable functions"
    - "function spaces for second order polynomial time"
    - "on function spaces and polynomial time computability"
    - "complexity theory for operators in analysis"
    - "parameterized uniform complexity in numerics from smooth to analytic from np hard to polytime"
    - "lipschitz continuous ordinary differential equations are polynomial space complete"

Connected through hyperedge HE-2 (induced by source paper "real number computability from the perspective of computer assisted proofs in analysis"):
  with members:
    - "parametrised second order complexity theory with applications to the study of interval computation"
    - "complexity theory for operators in analysis"
    - "on the complexity of real functions"

Connected through hyperedge HE-3 (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 with non rational coefficients"
    - "computing with polynomial ordinary differential equations"
    - "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"

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)