human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "just take the average an embarrassingly simple 2 n time algorithm for svp and cvp"
  Abstract: We show a $2^{n+o(n)}$-time (and space) algorithm for the Shortest Vector Problem on lattices (SVP) that works by repeatedly running an embarrassingly simple "pair and average" sieving-like procedure on a list of lattice vectors. This matches the running time (and space) of the current fastest known algorithm, due to Aggarwal, Dadush, Regev, and Stephens-Davidowitz (ADRS, in STOC, 2015), with a far simpler algorithm. Our algorithm is in fact a modification of the ADRS algorithm, with a certain careful rejection sampling step removed. #R##N#The correctness of our algorithm follows from a more general "meta-theorem," showing that such rejection sampling steps are unnecessary for a certain class of algorithms and use cases. In particular, this also applies to the related $2^{n + o(n)}$-time algorithm for the Closest Vector Problem (CVP), due to Aggarwal, Dadush, and Stephens-Davidowitz (ADS, in FOCS, 2015), yielding a similar embarrassingly simple algorithm for $\gamma$-approximate CVP for any $\gamma = 1+2^{-o(n/\log n)}$. (We can also remove the rejection sampling procedure from the $2^{n+o(n)}$-time ADS algorithm for exact CVP, but the resulting algorithm is still quite complicated.)

Connected through hyperedge HE-1 (induced by source paper "faster provable sieving algorithms for the shortest vector problem and the closest vector problem on lattices in 120001 p norm"):
  with members:
    - "the projection games conjecture and the hardness of approximation of ssat and related problems"
    - "improved algorithms for the shortest vector problem and the closest vector problem in the infinity norm"
    - "gap s eth hardness of svp"
    - "on the quantitative hardness of cvp"
    - "solving the shortest vector problem in 2 n time via discrete gaussian sampling"
    - "near optimal deterministic algorithms for volume computation via m ellipsoids"
    - "classical hardness of learning with errors"
    - "tensor based hardness of the shortest vector problem to within almost polynomial factors"

Connected through hyperedge HE-2 (induced by source paper "covering convex bodies and the closest vector problem"):
  with members:
    - "on compact representations of voronoi cells of lattices"
    - "a deterministic polynomial space construction for eps nets under any norm"
    - "lattice sparsification and the approximate closest vector problem"
    - "a o 1 eps 2 n time sieving algorithm for approximate integer programming"
    - "covering cubes and the closest vector problem"
    - "enumerative lattice algorithms in any norm via m ellipsoid coverings"

Connected through hyperedge HE-3 (induced by source paper "gap s eth hardness of svp"):
  with members:
    - "on the quantitative hardness of cvp"
    - "a birthday repetition theorem and complexity of approximating dense csps"
    - "discrete gaussian sampling reduces to cvp and svp"
    - "solving the shortest vector problem in 2 n time via discrete gaussian sampling"
    - "tensor based hardness of the shortest vector problem to within almost polynomial factors"
    - "enumerative lattice algorithms in any norm via m ellipsoid coverings"

Connected through hyperedge HE-4 (induced by source paper "improved algorithms for the shortest vector problem and the closest vector problem in the infinity norm"):
  with members:
    - "on the quantitative hardness of cvp"
    - "solving the shortest vector problem in 2 n time via discrete gaussian sampling"
    - "covering cubes and the closest vector problem"

Connected through hyperedge HE-5 (induced by source paper "on compact representations of voronoi cells of lattices"):
  with members:
    - "finding a closest point in a lattice of voronoi s first kind"
    - "short paths on the voronoi graph and the closest vector problem with preprocessing"

Question: Please tell me which class the center node 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)