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 "approximate degree secret sharing and concentration phenomena"):
  Abstract: The $\epsilon$-approximate degree $deg_\epsilon(f)$ of a Boolean function $f$ is the least degree of a real-valued polynomial that approximates $f$ pointwise to error $\epsilon$. The approximate degree of $f$ is at least $k$ iff there exists a pair of probability distributions, also known as a dual polynomial, that are perfectly $k$-wise indistinguishable, but are distinguishable by $f$ with advantage $1 - \epsilon$. Our contributions are: #R##N#We give a simple new construction of a dual polynomial for the AND function, certifying that $deg_\epsilon(f) \geq \Omega(\sqrt{n \log 1/\epsilon})$. This construction is the first to extend to the notion of weighted degree, and yields the first explicit certificate that the $1/3$-approximate degree of any read-once DNF is $\Omega(\sqrt{n})$. #R##N#We show that any pair of symmetric distributions on $n$-bit strings that are perfectly $k$-wise indistinguishable are also statistically $K$-wise indistinguishable with error at most $K^{3/2} \cdot \exp(-\Omega(k^2/K))$ for all $k < K < n/64$. This implies that any symmetric function $f$ is a reconstruction function with constant advantage for a ramp secret sharing scheme that is secure against size-$K$ coalitions with statistical error $K^{3/2} \exp(-\Omega(deg_{1/3}(f)^2/K))$ for all values of $K$ up to $n/64$ simultaneously. Previous secret sharing schemes required that $K$ be determined in advance, and only worked for $f=$ AND. #R##N#Our analyses draw new connections between approximate degree and concentration phenomena. #R##N#As a corollary, we show that for any $d   deg_{1/3}(f)$. These upper and lower bounds were also previously only known in the case $f=$ AND.

Includes members:
  Member "near optimal lower bounds on the threshold degree and sign rank of ac 0":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sign rank can increase under intersection")
  Member "a nearly optimal lower bound on the approximate degree of ac 0":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "near optimal lower bounds on the threshold degree and sign rank of ac 0")
  Member "dual lower bounds for approximate degree and markov bernstein inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "near optimal lower bounds on the threshold degree and sign rank of ac 0")
  Member "faster private release of marginals on small databases":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the power of factorization mechanisms in local and central differential privacy")
  Member "hardness amplification and the approximate degree of constant depth circuits":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "near optimal lower bounds on the threshold degree and sign rank of ac 0")
  Member "on the number of iterations for dantzig wolfe optimization and packing covering approximation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "simple and optimal high probability bounds for strongly convex stochastic gradient descent")

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)