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: "discrete gaussian sampling reduces to cvp and svp"
  Abstract: The discrete Gaussian $D_{L- t, s}$ is the distribution that assigns to each vector $x$ in a shifted lattice $L - t$ probability proportional to $e^{-\pi \|x\|^2/s^2}$. It has long been an important tool in the study of lattices. More recently, algorithms for discrete Gaussian sampling (DGS) have found many applications in computer science. In particular, polynomial-time algorithms for DGS with very high parameters $s$ have found many uses in cryptography and in reductions between lattice problems. And, in the past year, Aggarwal, Dadush, Regev, and Stephens-Davidowitz showed $2^{n+o(n)}$-time algorithms for DGS with a much wider range of parameters and used them to obtain the current fastest known algorithms for the two most important lattice problems, the Shortest Vector Problem (SVP) and the Closest Vector Problem (CVP). #R##N#Motivated by its increasing importance, we investigate the complexity of DGS itself and its relationship to CVP and SVP. Our first result is a polynomial-time dimension-preserving reduction from DGS to CVP. There is a simple reduction from CVP to DGS, so this shows that DGS is equivalent to CVP. Our second result, which we find to be more surprising, is a polynomial-time dimension-preserving reduction from centered DGS (the important special case when $ t = 0$) to SVP. In the other direction, there is a simple reduction from $\gamma$-approximate SVP for any $\gamma = \Omega(\sqrt{n/\log n})$, and we present some (relatively weak) evidence to suggest that this might be the best achievable approximation factor. #R##N#We also show that our CVP result extends to a much wider class of distributions and even to other norms.

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"
    - "solving the closest vector problem in 2 n time the discrete gaussian strikes again"
    - "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 "improved provable algorithms for the shortest vector problem via bounded distance decoding"):
  with members:
    - "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-3 (induced by source paper "deterministic sampling decoding where sphere decoding meets lattice gaussian distribution"):
  with members:
    - "lattice gaussian sampling by markov chain monte carlo bounded distance decoding and trapdoor sampling"
    - "on the geometric ergodicity of metropolis hastings algorithms 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"
    - "decoding by embedding correct decoding radius and dmt optimality"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "decoding by sampling part ii derandomization and soft output decoding"

Connected through hyperedge HE-4 (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"
    - "solving the closest vector problem in 2 n time the discrete gaussian strikes again"
    - "on the closest vector problem with a distance guarantee"

Connected through hyperedge HE-5 (induced by source paper "gap s eth hardness of svp"):
  with members:
    - "just take the average an embarrassingly simple 2 n time algorithm for svp and cvp"
    - "on the quantitative hardness of cvp"
    - "a birthday repetition theorem and complexity of approximating dense csps"
    - "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-6 (induced by source paper "solving the closest vector problem in 2 n time the discrete gaussian strikes again"):
  with members:
    - "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"
    - "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-7 (induced by source paper "on the geometric ergodicity of metropolis hastings algorithms for lattice gaussian sampling"):
  with members:
    - "lattice gaussian sampling by markov chain monte carlo convergence rate and decoding complexity"
    - "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"
    - "semantically secure lattice codes for the gaussian wiretap channel"
    - "achieving awgn channel capacity with lattice gaussian coding"
    - "decoding by sampling part ii derandomization and soft output decoding"
    - "on the proximity factors of lattice reduction aided decoding"
    - "decoding by sampling a randomized lattice algorithm for bounded distance decoding"

Connected through hyperedge HE-8 (induced by source paper "search to decision reductions for lattice problems with approximation factors slightly greater than one"):
  with members:
    - "solving the closest vector problem in 2 n time the discrete gaussian strikes again"
    - "on the closest vector problem with a distance guarantee"
    - "improved hardness results for unique shortest vector problem"

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.CC(Computational Complexity)