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: "efficient deterministic approximate counting for low degree polynomial threshold functions"
  Abstract: We give a deterministic algorithm for approximately counting satisfying assignments of a degree-$d$ polynomial threshold function (PTF). Given a degree-$d$ input polynomial $p(x_1,\dots,x_n)$ over $R^n$ and a parameter $\epsilon> 0$, our algorithm approximates $\Pr_{x \sim \{-1,1\}^n}[p(x) \geq 0]$ to within an additive $\pm \epsilon$ in time $O_{d,\epsilon}(1)\cdot \mathop{poly}(n^d)$. (Any sort of efficient multiplicative approximation is impossible even for randomized algorithms assuming $NP\not=RP$.) Note that the running time of our algorithm (as a function of $n^d$, the number of coefficients of a degree-$d$ PTF) is a \emph{fixed} polynomial. The fastest previous algorithm for this problem (due to Kane), based on constructions of unconditional pseudorandom generators for degree-$d$ PTFs, runs in time $n^{O_{d,c}(1) \cdot \epsilon^{-c}}$ for all $c > 0$. #R##N#The key novel contributions of this work are: A new multivariate central limit theorem, proved using tools from Malliavin calculus and Stein's Method. This new CLT shows that any collection of Gaussian polynomials with small eigenvalues must have a joint distribution which is very close to a multidimensional Gaussian distribution. A new decomposition of low-degree multilinear polynomials over Gaussian inputs. Roughly speaking we show that (up to some small error) any such polynomial can be decomposed into a bounded number of multilinear polynomials all of which have extremely small eigenvalues. We use these new ingredients to give a deterministic algorithm for a Gaussian-space version of the approximate counting problem, and then employ standard techniques for working with low-degree PTFs (invariance principles and regularity lemmas) to reduce the original approximate counting problem over the Boolean hypercube to the Gaussian version.

Connected through hyperedge HE-1 (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"
    - "better pseudorandom generators from milder pseudorandom restrictions"
    - "fooling functions of halfspaces under product distributions"
    - "bounded independence fools halfspaces"
    - "pseudorandom generators for polynomial threshold functions"
    - "bounded independence fools degree 2 threshold functions"

Connected through hyperedge HE-2 (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"
    - "an invariance principle for polytopes"
    - "a robust khintchine inequality and algorithms for computing optimal constants in fourier analysis and high dimensional geometry"
    - "fooling functions of halfspaces under product distributions"

Connected through hyperedge HE-3 (induced by source paper "non interactive simulation of correlated distributions is decidable"):
  with members:
    - "decidability of non interactive simulation of joint distributions"
    - "on non interactive simulation of joint distributions"
    - "lower bounds on the size of semidefinite programming relaxations"
    - "deterministic approximate counting for juntas of degree 2 polynomial threshold functions"
    - "a regularity lemma and low weight approximators for low degree polynomial threshold functions"

Connected through hyperedge HE-4 (induced by source paper "deterministic approximate counting for degree 2 polynomial threshold functions"):
  with members:
    - "deterministic approximate counting for juntas of degree 2 polynomial threshold functions"
    - "a pseudorandom generator for polynomial threshold functions of gaussian with subpolynomial seed length"
    - "the inverse shapley value problem"
    - "nearly optimal solutions for the chow parameters problem and low weight approximation of halfspaces"
    - "correlation decay up to uniqueness in spin systems"
    - "a small prg for polynomial threshold functions of gaussians"
    - "hardness results for agnostically learning low degree polynomial threshold functions"
    - "agnostic learning of monomials by halfspaces is hard"

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)