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: "short paths on the voronoi graph and the closest vector problem with preprocessing"
  Abstract: Improving on the Voronoi cell based techniques of Micciancio and Voulgaris (SIAM J. Comp. 13), and Sommer, Feder and Shalvi (SIAM J. Disc. Math. 09), we give a Las Vegas $\tilde{O}(2^n)$ expected time and space algorithm for CVPP (the preprocessing version of the Closest Vector Problem, CVP). This improves on the $\tilde{O}(4^n)$ deterministic runtime of the Micciancio Voulgaris algorithm, henceforth MV, for CVPP (which also solves CVP) at the cost of a polynomial amount of randomness (which only affects runtime, not correctness). As in MV, our algorithm proceeds by computing a short path on the Voronoi graph of the lattice, where lattice points are adjacent if their Voronoi cells share a common facet, from the origin to a closest lattice vector. #R##N#Our main technical contribution is a randomized procedure that given the Voronoi relevant vectors of a lattice - the lattice vectors inducing facets of the Voronoi cell - as preprocessing and any "close enough" lattice point to the target, computes a path to a closest lattice vector of expected polynomial size. This improves on the $\tilde{O}(4^n)$ path length given by the MV algorithm. Furthermore, as in MV, each edge of the path can be computed using a single iteration over the Voronoi relevant vectors. As a byproduct of our work, we also give an optimal relationship between geometric and path distance on the Voronoi graph, which we believe to be of independent interest.

Connected through hyperedge HE-1 (induced by source paper "on compact representations of voronoi cells of lattices"):
  with members:
    - "just take the average an embarrassingly simple 2 n time algorithm for svp and cvp"
    - "finding a closest point in a lattice of voronoi s first kind"

Connected through hyperedge HE-2 (induced by source paper "on the quantitative hardness of cvp"):
  with members:
    - "on problems as hard as cnf sat"
    - "strong eth breaks with merlin and arthur short non interactive proofs of batch evaluation"
    - "a birthday repetition theorem and complexity of approximating dense csps"
    - "solving the closest vector problem in 2 n time the discrete gaussian strikes again"
    - "powers of tensors and fast matrix multiplication"
    - "edit distance cannot be computed in strongly subquadratic time unless seth is false"
    - "solving the shortest vector problem in 2 n time via discrete gaussian sampling"
    - "on the closest vector problem with a distance guarantee"

Connected through hyperedge HE-3 (induced by source paper "lattice based locality sensitive hashing is optimal"):
  with members:
    - "tight lower bounds for data dependent locality sensitive hashing"
    - "solving the closest vector problem in 2 n time the discrete gaussian strikes again"
    - "finding a closest point in a lattice of voronoi s first kind"
    - "optimal lower bounds for locality sensitive hashing except when q is tiny"

Connected through hyperedge HE-4 (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"
    - "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"
    - "lattice sparsification and the approximate closest vector problem"
    - "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-5 (induced by source paper "solving the shortest vector problem in 2 n time via discrete gaussian sampling"):
  with members:
    - "discrete gaussian sampling reduces to cvp and svp"
    - "solving the closest vector problem in 2 n time the discrete gaussian strikes again"
    - "on the lattice smoothing parameter problem"
    - "on the closest vector problem with a distance guarantee"

Connected through hyperedge HE-6 (induced by source paper "on the shadow simplex method for curved polyhedra"):
  with members:
    - "geometric random edge"
    - "finding short paths on polytopes by the shadow vertex algorithm"
    - "beyond hirsch conjecture walks on random polytopes and smoothed complexity of the simplex method"
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"

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)