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: "almost optimal pseudorandom generators for spherical caps"
  Abstract: Halfspaces or linear threshold functions are widely studied in complexity theory, learning theory and algorithm design. In this work we study the natural problem of constructing pseudorandom generators (PRGs) for halfspaces over the sphere, aka spherical caps, which besides being interesting and basic geometric objects, also arise frequently in the analysis of various randomized algorithms (e.g., randomized rounding). We give an explicit PRG which fools spherical caps within error $\epsilon$ and has an almost optimal seed-length of $O(\log n + \log(1/\epsilon) \cdot \log\log(1/\epsilon))$. For an inverse-polynomially growing error $\epsilon$, our generator has a seed-length optimal up to a factor of $O( \log \log {(n)})$. The most efficient PRG previously known (due to Kane, 2012) requires a seed-length of $\Omega(\log^{3/2}{(n)})$ in this setting. We also obtain similar constructions to fool halfspaces with respect to the Gaussian distribution. #R##N#Our construction and analysis are significantly different from previous works on PRGs for halfspaces and build on the iterative dimension reduction ideas of Kane et. al. (2011) and Celis et. al. (2013), the \emph{classical moment problem} from probability theory and explicit constructions of \emph{orthogonal designs} based on the seminal work of Bourgain and Gamburd (2011) on expansion in Lie groups.

Connected through hyperedge HE-1 (induced by source paper "beating the perils of non convexity guaranteed training of neural networks using tensor methods"):
  with members:
    - "sample complexity analysis for learning overcomplete latent variable models through tensor methods"
    - "ell_1 regularized neural networks are improperly learnable in polynomial time"
    - "global optimality in tensor factorization deep learning and beyond"
    - "train faster generalize better stability of stochastic gradient descent"
    - "on the computational efficiency of training neural networks"
    - "provable methods for training neural networks with sparse connectivity"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"

Connected through hyperedge HE-2 (induced by source paper "quantified derandomization of linear threshold circuits"):
  with members:
    - "deterministic search for cnf satisfying assignments in almost polynomial time"
    - "average case lower bounds and satisfiability algorithms for small threshold circuits"
    - "randomness extraction in ac0 and with small locality"
    - "pseudorandomness via the discrete fourier transform"
    - "super linear gate and super quadratic wire lower bounds for depth two and depth three threshold circuits"
    - "analysis of boolean functions"
    - "new algorithms and lower bounds for circuits with linear threshold gates"
    - "a satisfiability algorithm for sparse depth two threshold circuits"

Connected through hyperedge HE-3 (induced by source paper "explicit correlation amplifiers for finding outlier correlations in deterministic subquadratic time"):
  with members:
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "scalability and total recall with fast coveringlsh"
    - "pseudorandomness via the discrete fourier transform"
    - "a faster subquadratic algorithm for finding outlier correlations"
    - "locality sensitive hashing without false negatives"
    - "optimal data dependent hashing for approximate near neighbors"
    - "probabilistic polynomials and hamming nearest neighbors"

Connected through hyperedge HE-4 (induced by source paper "pseudorandomness via the discrete fourier transform"):
  with members:
    - "pseudorandomness for concentration bounds and signed majorities"
    - "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"

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)