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: "targeted pseudorandom generators simulation advice generators and derandomizing logspace"
  Abstract: Assume that for every derandomization result for logspace algorithms, there is a pseudorandom generator strong enough to nearly recover the derandomization by iterating over all seeds and taking a majority vote. We prove under a precise version of this assumption that $\mathbf{BPL} \subseteq \bigcap_{\alpha > 0} \mathbf{DSPACE}(\log^{1 + \alpha} n)$. #R##N#We strengthen the theorem to an equivalence by considering two generalizations of the concept of a pseudorandom generator against logspace. A targeted pseudorandom generator against logspace takes as input a short uniform random seed and a finite automaton; it outputs a long bitstring that looks random to that particular automaton. A simulation advice generator for logspace stretches a small uniform random seed into a long advice string; the requirement is that there is some logspace algorithm that, given a finite automaton and this advice string, simulates the automaton reading a long uniform random input. We prove that $\bigcap_{\alpha > 0} \mathbf{promise\mbox{-}BPSPACE}(\log^{1 + \alpha} n) = \bigcap_{\alpha > 0} \mathbf{promise\mbox{-}DSPACE}(\log^{1 + \alpha} n)$ if and only if for every targeted pseudorandom generator against logspace, there is a simulation advice generator for logspace with similar parameters. #R##N#Finally, we observe that in a certain uniform setting (namely, if we only worry about sequences of automata that can be generated in logspace), targeted pseudorandom generators against logspace can be transformed into simulation advice generators with similar parameters.

Connected through hyperedge HE-1 (induced by source paper "optimal error pseudodistributions for read once branching programs"):
  with members:
    - "pseudorandom generators for width 3 branching programs"
    - "revisiting norm estimation in data streams"

Connected through hyperedge HE-2 (induced by source paper "preserving randomness for adaptive algorithms"):
  with members:
    - "the ladder a reliable leaderboard for machine learning competitions"
    - "generalization in adaptive data analysis and holdout reuse"
    - "algorithmic stability for adaptive data analysis"
    - "analysis of boolean functions"
    - "preserving statistical validity in adaptive data analysis"
    - "interactive fingerprinting codes and the hardness of preventing false discovery"
    - "fuzzy extractors how to generate strong keys from biometrics and other noisy data"

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)