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: "resource bounded measure"
  Abstract: A general theory of resource-bounded measurability and measure is developed. Starting from any feasible probability measure $\nu$ on the Cantor space $\C$ and any suitable complexity class $C \subseteq \C$, the theory identifies the subsets of $\C$ that are $\nu$-measurable in $C$ and assigns measures to these sets, thereby endowing $C$ with internal measure-theoretic structure. Classes to which the theory applies include various exponential time and space complexity classes, the class of all decidable languages, and the Cantor space itself, on which the resource-bounded theory is shown to agree with the classical theory. #R##N#The sets that are $\nu$-measurable in $C$ are shown to form an algebra relative to which $\nu$-measure is well-behaved. This algebra is also shown to be complete and closed under sufficiently uniform infinitary unions and intersections, and $\nu$-measure in $C$ is shown to have the appropriate additivity and monotone convergence properties with respect to such infinitary operations. #R##N#A generalization of the classical Kolmogorov zero-one law is proven, showing that when $\nu$ is any feasible coin-toss probability measure on $\C$, every set that is $\nu$-measurable in $C$ and (like most complexity classes) invariant under finite alterations must have $\nu$-measure 0 or $\nu$-measure 1 in $C$. #R##N#The theory is presented here is based on resource-bounded martingale splitting operators, which are type-2 functionals, each of which maps $\N \times {\cal D}_\nu$ into ${\cal D}_\nu \times {\cal D}_\nu$, where ${\cal D}_\nu$ is the set of all $\nu$-martingales. This type-2 aspect of the theory appears to be essential for general $\nu$-measure in complexity classes $C$, but the sets of $\nu$-measure 0 or 1 in C are shown to be characterized by the success conditions for martingales (type-1 functions) that have been used in resource-bounded measure to date.

Connected through hyperedge HE-1 (induced by source paper "optimal polynomial time estimators a bayesian notion of approximation algorithm"):
  with members:
    - "toward idealized decision theory"
    - "sum of squares proofs and the quest toward optimal algorithms"
    - "probabilities on sentences in an expressive logic"

Connected through hyperedge HE-2 (induced by source paper "polynomial space randomness in analysis"):
  with members:
    - "lines missing every random point"

Connected through hyperedge HE-3 (induced by source paper "axiomatizing resource bounds for measure"):
  with members:
    - "adventures in time and space"

Connected through hyperedge HE-4 (induced by source paper "resource bounded dimension in computational learning theory"):
  with members:
    - "online learning and resource bounded dimension winnow yields new lower bounds for hard sets"
    - "gales suffice for constructive dimension"
    - "dimension in complexity classes"
    - "the dimensions of individual strings and sequences"

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)