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: "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.

Connected through hyperedge HE-1 (induced by source paper "hardness of bounded distance decoding on lattices in ell_p norms"):
  with members:
    - "fine grained hardness of cvp p everything that we can prove and nothing else"
    - "gap s eth hardness of svp"
    - "on the quantitative hardness of cvp"
    - "discrete gaussian sampling reduces to cvp and svp"
    - "on the closest vector problem with a distance guarantee"
    - "tensor based hardness of the shortest vector problem to within almost polynomial factors"

Connected through hyperedge HE-2 (induced by source paper "a time distance trade off for gdd with preprocessing instantiating the dlw heuristic"):
  with members:
    - "sieving for closest lattice vectors with preprocessing"
    - "discrete gaussian sampling reduces to cvp and svp"
    - "on the closest vector problem with a distance guarantee"

Connected through hyperedge HE-3 (induced by source paper "on computing centroids according to the p norms of hamming distance vectors"):
  with members:
    - "collective schedules scheduling meets computational social choice"
    - "committee scoring rules axiomatic characterization and hierarchy"
    - "a generalization of the minisum and minimax voting methods"
    - "a note on clustering aggregation"
    - "combinatorial n fold integer programming and applications"
    - "lower bounds for approximation schemes for closest string"
    - "enumerative lattice algorithms in any norm via m ellipsoid coverings"

Connected through hyperedge HE-4 (induced by source paper "lattice gaussian sampling by markov chain monte carlo convergence rate and decoding complexity"):
  with members:
    - "independent metropolis hastings klein algorithm for lattice gaussian sampling"
    - "optimized markov chain monte carlo for signal detection in mimo systems an analysis of the stationary distribution and mixing time"
    - "markov chain monte carlo algorithms for lattice gaussian sampling"
    - "massive mimo for next generation wireless systems"
    - "semantically secure lattice codes for the gaussian wiretap channel"
    - "on the closest vector problem with a distance guarantee"
    - "decoding by embedding correct decoding radius and dmt optimality"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-5 (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"
    - "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"
    - "short paths on the voronoi graph and the closest vector problem with preprocessing"
    - "on the closest vector problem with a distance guarantee"

Connected through hyperedge HE-6 (induced by source paper "lattice based locality sensitive hashing is optimal"):
  with members:
    - "tight lower bounds for data dependent locality sensitive hashing"
    - "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"
    - "optimal lower bounds for locality sensitive hashing except when q is tiny"

Connected through hyperedge HE-7 (induced by source paper "space efficient classical and quantum algorithms for the shortest vector problem"):
  with members:
    - "solving the shortest vector problem in 2 n time via discrete gaussian sampling"
    - "on the closest vector problem with a distance guarantee"
    - "tensor based hardness of the shortest vector problem to within almost polynomial factors"
    - "improved analysis of kannan s shortest lattice vector algorithm"

Connected through hyperedge HE-8 (induced by source paper "sieving for closest lattice vectors with preprocessing"):
  with members:
    - "tradeoffs for nearest neighbors on the sphere"
    - "solving the shortest vector problem in 2 n time via discrete gaussian sampling"
    - "hashing for similarity search a survey"
    - "on the closest vector problem with a distance guarantee"

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)