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 "faster provable sieving algorithms for the shortest vector problem and the closest vector problem on lattices in 120001 p norm"):
  Abstract: In this paper we give provable sieving algorithms for the Shortest Vector Problem (SVP) and the Closest Vector Problem (CVP) on lattices in $\ell_p$ norm for $1\leq p\leq\infty$. The running time we get is better than existing provable sieving algorithms, except the Discrete Gaussian based algorithm by Aggarwal et al.[2015], but this algorithm works only for the Euclidean norm. We build on the randomized sieving framework of Ajtai, Kumar and Sivakumar [2001,2002], where they used a sieving sub-routine that runs in time quadratic in the number of sampled vectors. We give a new sieving procedure that works for all $\ell_p$ norm and runs in time linear in the number of sampled vectors. The main idea is to divide the space (hypersphere) into regions (hypercubes) such that each vector can be mapped efficiently to a region. This is an extension of the sieving technique in Aggarwal and Mukhopadhyay[2018], where it has been used only for the $\ell_{\infty}$ norm. This improves the time complexity of the overall algorithm as compared to previous analyses of AKS algorithm in $\ell_p$ norm [Blomer and Naewe(2009),Arvind and Joglekar(2008)]. In the $\ell_2$ norm, using the birthday paradox we achieve a time complexity of $2^{2.49n+o(n)}$, as compared to the time complexity of $2^{2.571n+o(n)}$ of AKS (using birthday paradox) [Hanrot et al.(2011)]. We further modify our linear sieving technique and introduce a mixed sieving procedure. At first a point is mapped to a hypercube within a sphere (as done in linear sieve) and then within each hypercube we perform a quadratic sieve like AKS. This helps us achieve a time complexity of $2^{2.25n+o(n)}$ in $\ell_2$ norm, which is better than the time complexity of List Sieve Birthday ($2^{2.465n+o(n)}$) [Pujol and Stehle(2009),Hanrot et al.(2011)]. We also adopt our techniques for approximation algorithms of the above problems.

Includes members:
  Member "the projection games conjecture and the hardness of approximation of ssat and related problems":
    also connected through other hyperedges:
      (none)
  Member "improved algorithms for the shortest vector problem and the closest vector problem in the infinity norm":
    also connected through other hyperedges:
      (none)
  Member "gap s eth hardness of 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 "just take the average an embarrassingly simple 2 n time algorithm for svp and cvp":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "covering convex bodies and the closest vector problem")
  Member "on the quantitative hardness of cvp":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "slide reduction revisited filling the gaps in svp approximation")
  Member "near optimal deterministic algorithms for volume computation via m ellipsoids":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multitype integer monoid optimization and applications")
  Member "classical hardness of learning with errors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the projection games conjecture and the hardness of approximation of ssat and related problems")

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)