human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "solving the closest vector problem in 2 n time the discrete gaussian strikes again":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hardness of bounded distance decoding on lattices in ell_p norms")
  Member "solving the shortest vector problem in 2 n time via discrete gaussian sampling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "faster provable sieving algorithms for the shortest vector problem and the closest vector problem on lattices in 120001 p norm")
  Member "on the closest vector problem with a distance guarantee":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hardness of bounded distance decoding on lattices in ell_p norms")
  Member "classical hardness of learning with errors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "faster provable sieving algorithms for the shortest vector problem and the closest vector problem on lattices in 120001 p norm")
  Member "tensor based hardness of the shortest vector problem to within almost polynomial factors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hardness of bounded distance decoding on lattices in ell_p norms")
  Member "lattice sparsification and the approximate closest vector problem":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "faster provable sieving algorithms for the shortest vector problem and the closest vector problem on lattices in 120001 p norm")
  Member "enumerative lattice algorithms in any norm via m ellipsoid coverings":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "faster provable sieving algorithms for the shortest vector problem and the closest vector problem on lattices in 120001 p norm")

Question: Please tell me which class the source paper 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)