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 "a polynomial time approximation scheme for fault tolerant distributed storage"):
  Abstract: We consider a problem which has received considerable attention in systems literature because of its applications to routing in delay tolerant networks and replica placement in distributed storage systems. In abstract terms the problem can be stated as follows: Given a random variable $X$ generated by a known product distribution over $\{0,1\}^n$ and a target value $0 \leq \theta \leq 1$, output a non-negative vector $w$, with $\|w\|_1 \le 1$, which maximizes the probability of the event $w \cdot X \ge \theta$. This is a challenging non-convex optimization problem for which even computing the value $\Pr[w \cdot X \ge \theta]$ of a proposed solution vector $w$ is #P-hard. #R##N#We provide an additive EPTAS for this problem which, for constant-bounded product distributions, runs in $ \poly(n) \cdot 2^{\poly(1/\eps)}$ time and outputs an $\eps$-approximately optimal solution vector $w$ for this problem. Our approach is inspired by, and extends, recent structural results from the complexity-theoretic study of linear threshold functions. Furthermore, in spite of the objective function being non-smooth, we give a \emph{unicriterion} PTAS while previous work for such objective functions has typically led to a \emph{bicriterion} PTAS. We believe our techniques may be applicable to get unicriterion PTAS for other non-smooth objective functions.

Includes members:
  Member "stochastic combinatorial optimization via poisson approximation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the markovian price of information")
  Member "nearly optimal solutions for the chow parameters problem and low weight approximation of halfspaces":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nearly tight bounds for robust proper learning of halfspaces with a margin")
  Member "maximizing expected utility for stochastic combinatorial optimization problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stochastic load balancing on unrelated machines")
  Member "symmetric allocations for distributed storage":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed storage allocations")
  Member "memory allocation in distributed storage networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a combinatorial view of the service rates of codes problem its equivalence to fractional matching and its connection with batch codes")
  Member "improved approximation of linear threshold functions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "nearly tight bounds for robust proper learning of halfspaces with a margin")
  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 "pseudorandom generators for polynomial threshold functions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "degree d chow parameters robustly determine degree d ptfs and algorithmic applications")

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.DS(Data Structures and Algorithms)