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: "dimensionally tight bounds for second order hamiltonian monte carlo"
  Abstract: Hamiltonian Monte Carlo (HMC) is a widely deployed method to sample from high-dimensional distributions in Statistics and Machine learning. HMC is known to run very efficiently in practice and its popular second-order "leapfrog" implementation has long been conjectured to run in $d^{1/4}$ gradient evaluations. Here we show that this conjecture is true when sampling from strongly log-concave target distributions that satisfy a weak third-order regularity property associated with the input data. Our regularity condition is weaker than the Lipschitz Hessian property and allows us to show faster convergence bounds for a much larger class of distributions than would be possible with the usual Lipschitz Hessian constant alone. Important distributions that satisfy our regularity condition include posterior distributions used in Bayesian logistic regression for which the data satisfies an "incoherence" property. Our result compares favorably with the best available bounds for the class of strongly log-concave distributions, which grow like $d^{{1}/{2}}$ gradient evaluations with the dimension. Moreover, our simulations on synthetic data suggest that, when our regularity condition is satisfied, leapfrog HMC performs better than its competitors -- both in terms of accuracy and in terms of the number of gradient evaluations it requires.

Connected through hyperedge HE-1 (induced by source paper "rapid convergence of the unadjusted langevin algorithm isoperimetry suffices"):
  with members:
    - "optimal convergence rate of hamiltonian monte carlo for strongly logconcave distributions"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "renyi differential privacy"
    - "convergence rate of riemannian hamiltonian monte carlo and faster polytope volume computation"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "concentrated differential privacy"
    - "renyi divergence and kullback leibler divergence"
    - "multiple source adaptation and the renyi divergence"

Connected through hyperedge HE-2 (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"
    - "algorithmic theory of odes and sampling from well conditioned logconcave densities"
    - "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-3 (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"
    - "algorithmic theory of odes and sampling from well conditioned logconcave densities"
    - "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-4 (induced by source paper "nonconvex sampling with the metropolis adjusted langevin algorithm"):
  with members:
    - "online sampling from log concave distributions"
    - "dimensionally tight running time 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"
    - "efficient learning of linear separators under bounded noise"

Connected through hyperedge HE-5 (induced by source paper "rapid convergence of the unadjusted langevin algorithm log sobolev suffices"):
  with members:
    - "nonconvex sampling with the metropolis adjusted langevin algorithm"
    - "optimal convergence rate of hamiltonian monte carlo for strongly logconcave distributions"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "convergence rate of riemannian hamiltonian monte carlo and faster polytope volume computation"
    - "concentrated differential privacy"
    - "renyi divergence and kullback leibler divergence"

Connected through hyperedge HE-6 (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"
    - "algorithmic theory of odes and sampling from well conditioned logconcave densities"
    - "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)