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: "lattice sparsification and the approximate closest vector problem"
  Abstract: We give a deterministic algorithm for solving the (1+eps)-approximate Closest Vector Problem (CVP) on any n dimensional lattice and any norm in 2^{O(n)}(1+1/eps)^n time and 2^n poly(n) space. Our algorithm builds on the lattice point enumeration techniques of Micciancio and Voulgaris (STOC 2010) and Dadush, Peikert and Vempala (FOCS 2011), and gives an elegant, deterministic alternative to the "AKS Sieve" based algorithms for (1+eps)-CVP (Ajtai, Kumar, and Sivakumar; STOC 2001 and CCC 2002). Furthermore, assuming the existence of a poly(n)-space and 2^{O(n)} time algorithm for exact CVP in the l_2 norm, the space complexity of our algorithm can be reduced to polynomial. #R##N#Our main technical contribution is a method for "sparsifying" any input lattice while approximately maintaining its metric structure. To this end, we employ the idea of random sublattice restrictions, which was first employed by Khot (FOCS 2003) for the purpose of proving hardness for Shortest Vector Problem (SVP) under l_p norms.

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"
    - "just take the average an embarrassingly simple 2 n time algorithm for svp and cvp"
    - "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"

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"
    - "just take the average an embarrassingly simple 2 n time algorithm for svp and cvp"
    - "a deterministic polynomial space construction for eps nets under any norm"
    - "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 "solving the closest vector problem in 2 n time the discrete gaussian strikes again"):
  with members:
    - "discrete gaussian sampling reduces to cvp and svp"
    - "solving the shortest vector problem in 2 n time via discrete gaussian sampling"
    - "short paths on the voronoi graph and the closest vector problem with preprocessing"
    - "on the closest vector problem with a distance guarantee"
    - "classical hardness of learning with errors"
    - "tensor based hardness of the shortest vector problem to within almost polynomial factors"
    - "approximating the closest vector problem using an approximate shortest vector oracle"
    - "enumerative lattice algorithms in any norm via m ellipsoid coverings"

Connected through hyperedge HE-4 (induced by source paper "discrete gaussian sampling reduces to cvp and svp"):
  with members:
    - "solving the closest vector problem in 2 n time the discrete gaussian strikes again"
    - "solving the shortest vector problem in 2 n time via discrete gaussian sampling"
    - "on the closest vector problem with a distance guarantee"
    - "classical hardness of learning with errors"
    - "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-5 (induced by source paper "on the closest vector problem with a distance guarantee"):
  with members:
    - "on the lattice smoothing parameter problem"
    - "tensor based hardness of the shortest vector problem to within almost polynomial factors"

Connected through hyperedge HE-6 (induced by source paper "a deterministic polynomial space construction for eps nets under any norm"):
  with members:
    - "near optimal deterministic algorithms for volume computation via m ellipsoids"
    - "enumerative lattice algorithms in any norm via m ellipsoid coverings"
    - "improved analysis of kannan s shortest lattice vector algorithm"

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)