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: "bounded independence fools degree 2 threshold functions"
  Abstract: Let x be a random vector coming from any k-wise independent distribution over {-1,1}^n. For an n-variate degree-2 polynomial p, we prove that E[sgn(p(x))] is determined up to an additive epsilon for k = poly(1/epsilon). This answers an open question of Diakonikolas et al. (FOCS 2009). Using standard constructions of k-wise independent distributions, we obtain a broad class of explicit generators that epsilon-fool the class of degree-2 threshold functions with seed length log(n)*poly(1/epsilon). #R##N#Our approach is quite robust: it easily extends to yield that the intersection of any constant number of degree-2 threshold functions is epsilon-fooled by poly(1/epsilon)-wise independence. Our results also hold if the entries of x are k-wise independent standard normals, implying for example that bounded independence derandomizes the Goemans-Williamson hyperplane rounding scheme. #R##N#To achieve our results, we introduce a technique we dub multivariate FT-mollification, a generalization of the univariate form introduced by Kane et al. (SODA 2010) in the context of streaming algorithms. Along the way we prove a generalized hypercontractive inequality for quadratic forms which takes the operator norm of the associated matrix into account. These techniques may be of independent interest.

Connected through hyperedge HE-1 (induced by source paper "stable sparse subspace embedding for dimensionality reduction"):
  with members:
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "recovering the optimal solution by dual random projection"
    - "random projections for linear support vector machines"
    - "low rank approximation and regression in input sparsity time"
    - "random projections for k means clustering"
    - "sparser johnson lindenstrauss transforms"

Connected through hyperedge HE-2 (induced by source paper "degree d chow parameters robustly determine degree d ptfs and algorithmic applications"):
  with members:
    - "learning geometric concepts with nasty noise"
    - "hardness of learning noisy halfspaces using polynomial thresholds"
    - "robust estimators in high dimensions without the computational intractability"
    - "pseudorandomness via the discrete fourier transform"
    - "complexity theoretic limitations on learning halfspaces"
    - "super linear gate and super quadratic wire lower bounds for depth two and depth three threshold circuits"
    - "a ptas for agnostically learning halfspaces"
    - "analysis of boolean functions"

Connected through hyperedge HE-3 (induced by source paper "fooling polytopes"):
  with members:
    - "fooling intersections of low weight halfspaces"
    - "pseudorandomness via the discrete fourier transform"
    - "analysis of boolean functions"
    - "the average sensitivity of an intersection of half spaces"
    - "an invariance principle for polytopes"
    - "a pseudorandom generator for polynomial threshold functions of gaussian with subpolynomial seed length"
    - "a small prg for polynomial threshold functions of gaussians"
    - "fooling functions of halfspaces under product distributions"

Connected through hyperedge HE-4 (induced by source paper "density estimation for shift invariant multidimensional distributions"):
  with members:
    - "learning sums of independent random variables with sparse collective support"
    - "resilience a criterion for learning in the presence of arbitrary outliers"
    - "robust estimators in high dimensions without the computational intractability"
    - "learning multivariate log concave distributions"
    - "the fourier transform of poisson multinomial distributions and its algorithmic applications"
    - "optimal learning via the fourier transform for sums of independent integer random variables"
    - "sample optimal density estimation in nearly linear time"
    - "near optimal sample estimators for spherical gaussian mixtures"

Connected through hyperedge HE-5 (induced by source paper "deterministic search for cnf satisfying assignments in almost polynomial time"):
  with members:
    - "on polynomial approximations to ac 0"
    - "pseudorandomness for regular branching programs via fourier analysis"
    - "efficient deterministic approximate counting for low degree polynomial threshold functions"
    - "better pseudorandom generators from milder pseudorandom restrictions"
    - "fooling functions of halfspaces under product distributions"
    - "bounded independence fools halfspaces"
    - "pseudorandom generators for polynomial threshold functions"

Connected through hyperedge HE-6 (induced by source paper "luby veli v c kovi c wigderson revisited improved correlation bounds and pseudorandom generators for depth two circuits"):
  with members:
    - "improved pseudorandom generators from pseudorandom multi switching lemmas"
    - "hardness results for agnostically learning low degree polynomial threshold functions"
    - "pseudorandom generators for polynomial threshold functions"

Connected through hyperedge HE-7 (induced by source paper "fooling intersections of low weight halfspaces"):
  with members:
    - "average case lower bounds and satisfiability algorithms for small threshold circuits"
    - "pseudorandomness via the discrete fourier transform"
    - "analysis of boolean functions"
    - "the average sensitivity of an intersection of half spaces"
    - "deterministic approximate counting for juntas of degree 2 polynomial threshold functions"
    - "efficient deterministic approximate counting for low degree polynomial threshold functions"
    - "an invariance principle for polytopes"
    - "a robust khintchine inequality and algorithms for computing optimal constants in fourier analysis and high dimensional geometry"

Connected through hyperedge HE-8 (induced by source paper "continuous monitoring of ell_p norms in data streams"):
  with members:
    - "bptree an ell_2 heavy hitters algorithm using constant memory"
    - "beating countsketch for heavy hitters in insertion streams"
    - "streaming symmetric norms via measure concentration"
    - "taylor polynomial estimator for estimating frequency moments"
    - "universal sketches for the frequency negative moments and other decreasing streaming sums"
    - "tracking the frequency moments at all times"
    - "fast moment estimation in data streams in optimal space"
    - "measuring independence of datasets"

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)