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 "solving the closest vector problem in 2 n time the discrete gaussian strikes again"):
  Abstract: We give a $2^{n+o(n)}$-time and space randomized algorithm for solving the exact Closest Vector Problem (CVP) on $n$-dimensional Euclidean lattices. This improves on the previous fastest algorithm, the deterministic $\widetilde{O}(4^{n})$-time and $\widetilde{O}(2^{n})$-space algorithm of Micciancio and Voulgaris. #R##N#We achieve our main result in three steps. First, we show how to modify the sampling algorithm from [ADRS15] to solve the problem of discrete Gaussian sampling over lattice shifts, $L- t$, with very low parameters. While the actual algorithm is a natural generalization of [ADRS15], the analysis uses substantial new ideas. This yields a $2^{n+o(n)}$-time algorithm for approximate CVP for any approximation factor $\gamma = 1+2^{-o(n/\log n)}$. Second, we show that the approximate closest vectors to a target vector $t$ can be grouped into "lower-dimensional clusters," and we use this to obtain a recursive reduction from exact CVP to a variant of approximate CVP that "behaves well with these clusters." Third, we show that our discrete Gaussian sampling algorithm can be used to solve this variant of approximate CVP. #R##N#The analysis depends crucially on some new properties of the discrete Gaussian distribution and approximate closest vectors, which might be of independent interest.

Includes members:
  Member "discrete gaussian sampling reduces to cvp and svp":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hardness of bounded distance decoding on lattices in ell_p norms")
  Member "solving the shortest vector problem in 2 n time via discrete gaussian sampling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "faster provable sieving algorithms for the shortest vector problem and the closest vector problem on lattices in 120001 p norm")
  Member "short paths on the voronoi graph and the closest vector problem with preprocessing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on compact representations of voronoi cells of lattices")
  Member "on the closest vector problem with a distance guarantee":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hardness of bounded distance decoding on lattices in ell_p norms")
  Member "classical hardness of learning with errors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "faster provable sieving algorithms for the shortest vector problem and the closest vector problem on lattices in 120001 p norm")
  Member "tensor based hardness of the shortest vector problem to within almost polynomial factors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hardness of bounded distance decoding on lattices in ell_p norms")
  Member "lattice sparsification and the approximate closest vector problem":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "faster provable sieving algorithms for the shortest vector problem and the closest vector problem on lattices in 120001 p norm")
  Member "approximating the closest vector problem using an approximate shortest vector oracle":
    also connected through other hyperedges:
      (none)

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)