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: "optimal convergence rate of hamiltonian monte carlo for strongly logconcave distributions"
  Abstract: We study Hamiltonian Monte Carlo (HMC) for sampling from a strongly logconcave density proportional to $e^{-f}$ where $f:\mathbb{R}^d \to \mathbb{R}$ is $\mu$-strongly convex and $L$-smooth (the condition number is $\kappa = L/\mu$). We show that the relaxation time (inverse of the spectral gap) of ideal HMC is $O(\kappa)$, improving on the previous best bound of $O(\kappa^{1.5})$; we complement this with an example where the relaxation time is $\Omega(\kappa)$. When implemented using a nearly optimal ODE solver, HMC returns an $\varepsilon$-approximate point in $2$-Wasserstein distance using $\widetilde{O}((\kappa d)^{0.5} \varepsilon^{-1})$ gradient evaluations per step and $\widetilde{O}((\kappa d)^{1.5}\varepsilon^{-1})$ total time.

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"
    - "computational separations between sampling and optimization"
    - "rapid convergence of the unadjusted langevin algorithm isoperimetry suffices"
    - "algorithmic theory of odes and sampling from well conditioned logconcave densities"

Connected through hyperedge HE-2 (induced by source paper "rapid convergence of the unadjusted langevin algorithm isoperimetry suffices"):
  with members:
    - "dimensionally tight bounds for second order hamiltonian monte carlo"
    - "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-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"
    - "algorithmic theory of odes and sampling from well conditioned logconcave densities"
    - "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"
    - "rapid convergence of the unadjusted langevin algorithm log sobolev suffices"
    - "algorithmic theory of odes and sampling from well conditioned logconcave densities"
    - "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 "rapid convergence of the unadjusted langevin algorithm log sobolev suffices"):
  with members:
    - "nonconvex sampling with the metropolis adjusted langevin algorithm"
    - "dimensionally tight bounds for second order hamiltonian monte carlo"
    - "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"

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)