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: "pseudorandomness for concentration bounds and signed majorities"
  Abstract: The problem of constructing pseudorandom generators that fool halfspaces has been studied intensively in recent times. For fooling halfspaces over the hypercube with polynomially small error, the best construction known requires seed-length O(log^2 n) (MekaZ13). Getting the seed-length down to O(log(n)) is a natural challenge in its own right, which needs to be overcome in order to derandomize RL. In this work we make progress towards this goal by obtaining near-optimal generators for two important special cases: #R##N#1) We give a near optimal derandomization of the Chernoff bound for independent, uniformly random bits. Specifically, we show how to generate a x in {1,-1}^n using $\tilde{O}(\log (n/\epsilon))$ random bits such that for any unit vector u,   matches the sub-Gaussian tail behaviour predicted by the Chernoff bound up to error eps. #R##N#2) We construct a generator which fools halfspaces with {0,1,-1} coefficients with error eps with a seed-length of $\tilde{O}(\log(n/\epsilon))$. This includes the important special case of majorities. #R##N#In both cases, the best previous results required seed-length of $O(\log n + \log^2(1/\epsilon))$. #R##N#Technically, our work combines new Fourier-analytic tools with the iterative dimension reduction techniques and the gradually increasing independence paradigm of previous works (KaneMN11, CelisRSW13, GopalanMRTV12).

Connected through hyperedge HE-1 (induced by source paper "pseudorandomness via the discrete fourier transform"):
  with members:
    - "almost optimal pseudorandom generators for spherical caps"
    - "inequalities and tail bounds for elementary symmetric polynomial with applications"
    - "an invariance principle for polytopes"
    - "a pseudorandom generator for polynomial threshold functions of gaussian with subpolynomial seed length"
    - "better pseudorandom generators from milder pseudorandom restrictions"
    - "a small prg for polynomial threshold functions of gaussians"
    - "fooling functions of halfspaces under product distributions"
    - "k independent gaussians fool polynomial threshold functions"

Connected through hyperedge HE-2 (induced by source paper "weighted polynomial approximations limits for learning and pseudorandomness"):
  with members:
    - "analysis of boolean functions"
    - "agnostic learning of disjunctions on symmetric distributions"
    - "approximate resilience monotonicity and the complexity of agnostic learning"
    - "pseudorandomness for regular branching programs via fourier analysis"
    - "the complexity of learning halfspaces using generalized linear methods"
    - "fooling functions of halfspaces under product distributions"
    - "a derandomized sparse johnson lindenstrauss transform"
    - "bounded independence fools halfspaces"

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)