human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deterministic approximate counting for juntas of degree 2 polynomial threshold functions"):
  Abstract: Let $g: \{-1,1\}^k \to \{-1,1\}$ be any Boolean function and $q_1,\dots,q_k$ be any degree-2 polynomials over $\{-1,1\}^n.$ We give a \emph{deterministic} algorithm which, given as input explicit descriptions of $g,q_1,\dots,q_k$ and an accuracy parameter $\eps>0$, approximates \[\Pr_{x \sim \{-1,1\}^n}[g(\sign(q_1(x)),\dots,\sign(q_k(x)))=1]\] to within an additive $\pm \eps$. For any constant $\eps > 0$ and $k \geq 1$ the running time of our algorithm is a fixed polynomial in $n$. This is the first fixed polynomial-time algorithm that can deterministically approximately count satisfying assignments of a natural class of depth-3 Boolean circuits. #R##N#Our algorithm extends a recent result \cite{DDS13:deg2count} which gave a deterministic approximate counting algorithm for a single degree-2 polynomial threshold function $\sign(q(x)),$ corresponding to the $k=1$ case of our result. #R##N#Our algorithm and analysis requires several novel technical ingredients that go significantly beyond the tools required to handle the $k=1$ case in \cite{DDS13:deg2count}. One of these is a new multidimensional central limit theorem for degree-2 polynomials in Gaussian random variables which builds on recent Malliavin-calculus-based results from probability theory. We use this CLT as the basis of a new decomposition technique for $k$-tuples of degree-2 Gaussian polynomials and thus obtain an efficient deterministic approximate counting algorithm for the Gaussian distribution. Finally, a third new ingredient is a "regularity lemma" for \emph{$k$-tuples} of degree-$d$ polynomial threshold functions. This generalizes both the regularity lemmas of \cite{DSTW:10,HKM:09} and the regularity lemma of Gopalan et al \cite{GOWZ10}. Our new regularity lemma lets us extend our deterministic approximate counting results from the Gaussian to the Boolean domain.

Includes members:
  Member "deterministic approximate counting for degree 2 polynomial threshold functions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient deterministic approximate counting for low degree polynomial threshold functions")
  Member "the inverse shapley value problem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "degree d chow parameters robustly determine degree d ptfs and algorithmic applications")
  Member "nearly optimal solutions for the chow parameters problem and low weight approximation of halfspaces":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nearly tight bounds for robust proper learning of halfspaces with a margin")
  Member "fooling functions of halfspaces under product distributions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "algorithms and lower bounds for de morgan formulas of low communication leaf gates")
  Member "hardness results for agnostically learning low degree polynomial threshold functions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "nearly tight bounds for robust proper learning of halfspaces with a margin")
  Member "k independent gaussians fool polynomial threshold functions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fooling polytopes")
  Member "bounded independence fools halfspaces":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "list decodable subspace recovery via sum of squares")
  Member "polynomial threshold functions structure approximation and pseudorandomness":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a ptas for agnostically learning halfspaces")

Question: Please tell me which class the source paper 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)