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: "convergence rate of riemannian hamiltonian monte carlo and faster polytope volume computation"
  Abstract: We give the first rigorous proof of the convergence of Riemannian Hamiltonian Monte Carlo, a general (and practical) method for sampling Gibbs distributions. Our analysis shows that the rate of convergence is bounded in terms of natural smoothness parameters of an associated Riemannian manifold. We then apply the method with the manifold defined by the log barrier function to the problems of (1) uniformly sampling a polytope and (2) computing its volume, the latter by extending Gaussian cooling to the manifold setting. In both cases, the total number of steps needed is O^{*}(mn^{\frac{2}{3}}), improving the state of the art. A key ingredient of our analysis is a proof of an analog of the KLS conjecture for Gibbs distributions over manifolds.

Connected through hyperedge HE-1 (induced by source paper "strong self concordance and sampling"):
  with members:
    - "faster polytope rounding sampling and volume computation via a sublinear ball walk"
    - "solving linear programs with sqrt rank linear system solves"
    - "geodesic walks in polytopes"
    - "the mixing time of the dikin walk in a polytope a simple proof"
    - "approximate undirected maximum flows in o m polylog n time"
    - "navigating central path with electrical flows from flows to matchings and back"
    - "nearly maximum flows in nearly linear time"
    - "fast approximation algorithms for cut based problems in undirected graphs"

Connected through hyperedge HE-2 (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"
    - "dimensionally tight bounds for second order hamiltonian monte carlo"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "renyi differential privacy"
    - "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"
    - "optimal convergence rate of hamiltonian monte carlo for strongly logconcave distributions"
    - "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"
    - "geodesic walks in polytopes"

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"
    - "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"
    - "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"
    - "dimensionally tight bounds for second order hamiltonian monte carlo"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "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"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "faster polytope rounding sampling and volume computation via a sublinear ball walk"):
  with members:
    - "geodesic walks in polytopes"

Connected through hyperedge HE-8 (induced by source paper "algorithmic theory of odes and sampling from well conditioned logconcave densities"):
  with members:
    - "dimensionally tight running time bounds for second order hamiltonian monte carlo"
    - "relative error tensor low rank approximation"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "geodesic walks in polytopes"
    - "new frameworks for offline and streaming coreset constructions"
    - "input sparsity and hardness for robust subspace approximation"
    - "complexity theory for operators in analysis"

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)