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: "algorithmic theory of odes and sampling from well conditioned logconcave densities"
  Abstract: Sampling logconcave functions arising in statistics and machine learning has been a subject of intensive study. Recent developments include analyses for Langevin dynamics and Hamiltonian Monte Carlo (HMC). While both approaches have dimension-independent bounds for the underlying $\mathit{continuous}$ processes under sufficiently strong smoothness conditions, the resulting discrete algorithms have complexity and number of function evaluations growing with the dimension. Motivated by this problem, in this paper, we give a general algorithm for solving multivariate ordinary differential equations whose solution is close to the span of a known basis of functions (e.g., polynomials or piecewise polynomials). The resulting algorithm has polylogarithmic depth and essentially tight runtime - it is nearly linear in the size of the representation of the solution. #R##N#We apply this to the sampling problem to obtain a nearly linear implementation of HMC for a broad class of smooth, strongly logconcave densities, with the number of iterations (parallel depth) and gradient evaluations being $\mathit{polylogarithmic}$ in the dimension (rather than polynomial as in previous work). This class includes the widely-used loss function for logistic regression with incoherent weight matrices and has been subject of much study recently. We also give a faster algorithm with $ \mathit{polylogarithmic~depth}$ for the more general and standard class of strongly convex functions with Lipschitz gradient. These results are based on (1) an improved contraction bound for the exact HMC process and (2) logarithmic bounds on the degree of polynomials that approximate solutions of the differential equations arising in implementing HMC.

Connected through hyperedge HE-1 (induced by source paper "logsmooth gradient concentration and tighter runtimes for metropolized hamiltonian monte carlo"):
  with members:
    - "the randomized midpoint method for log concave sampling"
    - "optimal convergence rate of hamiltonian monte carlo for strongly logconcave distributions"
    - "computational separations between sampling and optimization"
    - "rapid convergence of the unadjusted langevin algorithm isoperimetry suffices"

Connected through hyperedge HE-2 (induced by source paper "efficient symmetric norm regression via linear sketching"):
  with members:
    - "on mean estimation for general norms with statistical queries"
    - "dimensionality reduction for tukey regression"
    - "tight bounds for ell_p oblivious subspace embeddings"
    - "subspace embedding and linear regression with orlicz norm"
    - "streaming space complexity of nearly all functions of one variable on frequency vectors"
    - "approximate near neighbors for general symmetric norms"
    - "input sparsity and hardness for robust subspace approximation"
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"

Connected through hyperedge HE-3 (induced by source paper "the randomized midpoint method for log concave sampling"):
  with members:
    - "faster algorithms for polytope rounding sampling and volume computation via a sublinear ball walk"
    - "optimal convergence rate of hamiltonian monte carlo for strongly logconcave distributions"
    - "dimensionally tight bounds for second order hamiltonian monte carlo"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "a tutorial on thompson sampling"
    - "convergence rate of riemannian hamiltonian monte carlo and faster polytope volume computation"
    - "geodesic walks in polytopes"

Connected through hyperedge HE-4 (induced by source paper "estimating normalizing constants for log concave distributions algorithms and lower bounds"):
  with members:
    - "the randomized midpoint method for log concave sampling"
    - "optimal convergence rate of hamiltonian monte carlo for strongly logconcave distributions"
    - "rapid convergence of the unadjusted langevin algorithm log sobolev suffices"
    - "dimensionally tight bounds for second order hamiltonian monte carlo"
    - "geodesic walks in polytopes"
    - "gaussian cooling and o n 3 algorithms for volume and gaussian volume"
    - "dispersion of mass and the complexity of randomized geometric algorithms"

Connected through hyperedge HE-5 (induced by source paper "optimal convergence rate of hamiltonian monte carlo for strongly logconcave distributions"):
  with members:
    - "rapid convergence of the unadjusted langevin algorithm log sobolev suffices"
    - "dimensionally tight bounds for second order hamiltonian monte carlo"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "convergence rate of riemannian hamiltonian monte carlo and faster polytope volume computation"

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)