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: "geodesic walks in polytopes"
  Abstract: We introduce the geodesic walk for sampling Riemannian manifolds and apply it to the problem of generating uniform random points from polytopes in R^n specified by m inequalities. The walk is a discrete-time simulation of a stochastic differential equation (SDE) on the Riemannian manifold equipped with the metric induced by the Hessian of a convex function; each step is the solution of an ordinary differential equation (ODE). The resulting sampling algorithm for polytopes mixes in O*(mn^{3/4}) steps. This is the first walk that breaks the quadratic barrier for mixing in high dimension, improving on the previous best bound of O*(mn) by Kannan and Narayanan for the Dikin walk. We also show that each step of the geodesic walk (solving an ODE) can be implemented efficiently, thus improving the time complexity for sampling polytopes. Our analysis of the geodesic walk for general Hessian manifolds does not assume positive curvature and might be of independent interest.

Connected through hyperedge HE-1 (induced by source paper "on the approximability of weighted model integration on dnf structures"):
  with members:
    - "learning to reason leveraging neural networks for approximate dnf counting"
    - "on hashing based approaches to approximate dnf counting"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "distribution aware sampling and weighted model counting for sat"
    - "approximate counting in smt and value estimation for probabilistic programs"
    - "approximate inference algorithms for hybrid bayesian networks with discrete constraints"
    - "probabilistic theorem proving"
    - "approximating the volume of unions and intersections of high dimensional geometric objects"

Connected through hyperedge HE-2 (induced by source paper "optimal anytime regret with two experts"):
  with members:
    - "krivine diffusions attain the goemans williamson approximation ratio"
    - "sticky brownian rounding and its applications to constraint satisfaction problems"
    - "tight lower bounds for multiplicative weights algorithmic families"
    - "towards optimal algorithms for prediction with expert advice"
    - "towards minimax online learning with unknown time horizon"

Connected through hyperedge HE-3 (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"
    - "convergence rate of riemannian hamiltonian monte carlo and faster polytope volume computation"
    - "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-4 (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"
    - "convergence rate of riemannian hamiltonian monte carlo and faster polytope volume computation"

Connected through hyperedge HE-5 (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"
    - "dimensionally tight bounds for second order hamiltonian monte carlo"
    - "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-6 (induced by source paper "faster polytope rounding sampling and volume computation via a sublinear ball walk"):
  with members:
    - "convergence rate of riemannian hamiltonian monte carlo and faster polytope volume computation"

Connected through hyperedge HE-7 (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"
    - "convergence rate of riemannian hamiltonian monte carlo and faster polytope volume computation"
    - "new frameworks for offline and streaming coreset constructions"
    - "input sparsity and hardness for robust subspace approximation"
    - "complexity theory for operators in analysis"

Connected through hyperedge HE-8 (induced by source paper "faster algorithms for computing the stationary distribution simulating random walks and more"):
  with members:
    - "sparsified cholesky and multigrid solvers for connection laplacians"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"
    - "constructing linear sized spectral sparsification in almost linear time"
    - "sparsified cholesky solvers for sdd linear systems"
    - "fast generation of random spanning trees and the effective resistance metric"
    - "effective resistance reducing flows spectrally thin trees and asymmetric tsp"
    - "approximate undirected maximum flows in o m polylog n time"
    - "fast ppr scaling personalized pagerank estimation for large graphs"

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)