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: "on the closest vector problem with a distance guarantee"
  Abstract: We present a substantially more efficient variant, both in terms of running time and size of preprocessing advice, of the algorithm by Liu, Lyubashevsky, and Micciancio for solving CVPP (the preprocessing version of the Closest Vector Problem, CVP) with a distance guarantee. For instance, for any $\alpha < 1/2$, our algorithm finds the (unique) closest lattice point for any target point whose distance from the lattice is at most $\alpha$ times the length of the shortest nonzero lattice vector, requires as preprocessing advice only $N \approx \widetilde{O}(n \exp(\alpha^2 n /(1-2\alpha)^2))$ vectors, and runs in time $\widetilde{O}(nN)$. #R##N#As our second main contribution, we present reductions showing that it suffices to solve CVP, both in its plain and preprocessing versions, when the input target point is within some bounded distance of the lattice. The reductions are based on ideas due to Kannan and a recent sparsification technique due to Dadush and Kun. Combining our reductions with the LLM algorithm gives an approximation factor of $O(n/\sqrt{\log n})$ for search CVPP, improving on the previous best of $O(n^{1.5})$ due to Lagarias, Lenstra, and Schnorr. When combined with our improved algorithm we obtain, somewhat surprisingly, that only O(n) vectors of preprocessing advice are sufficient to solve CVPP with (the only slightly worse) approximation factor of O(n).

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"
    - "solving the closest vector problem in 2 n time the discrete gaussian strikes again"
    - "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"
    - "solving the closest vector problem in 2 n time the discrete gaussian strikes again"

Connected through hyperedge HE-3 (induced by source paper "awgn goodness is enough capacity achieving lattice codes based on dithered probabilistic shaping"):
  with members:
    - "fundamental limits of communication with low probability of detection"
    - "covert communication over noisy channels a resolvability perspective"
    - "on probabilistic shaping of quadrature amplitude modulation for the nonlinear fiber channel"
    - "algebraic lattice codes achieve the capacity of the compound block fading channel"
    - "lda lattices without dithering achieve capacity on the gaussian channel"
    - "almost universal codes for fading wiretap channels"
    - "construction of capacity achieving lattice codes polar lattices"
    - "on the lattice smoothing parameter problem"

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"
    - "solving the closest vector problem in 2 n time the discrete gaussian strikes again"
    - "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"
    - "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"
    - "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"
    - "short paths on the voronoi graph and the closest vector problem with preprocessing"

Connected through hyperedge HE-6 (induced by source paper "space efficient classical and quantum algorithms for the shortest vector problem"):
  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"
    - "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-7 (induced by source paper "sieving for closest lattice vectors with preprocessing"):
  with members:
    - "tradeoffs for nearest neighbors on the sphere"
    - "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"
    - "hashing for similarity search a survey"

Connected through hyperedge HE-8 (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"
    - "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"

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)