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: "rapid convergence of the unadjusted langevin algorithm log sobolev suffices"
  Abstract: We prove a convergence guarantee on the unadjusted Langevin algorithm for sampling assuming only that the target distribution $e^{-f}$ satisfies a log-Sobolev inequality and the Hessian of $f$ is bounded. In particular, $f$ is not required to be convex or have higher derivatives bounded.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "optimal convergence rate of hamiltonian monte carlo for strongly logconcave distributions"):
  with members:
    - "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"
    - "convergence rate of riemannian hamiltonian monte carlo and faster polytope volume computation"

Connected through hyperedge HE-3 (induced by source paper "simulated tempering langevin monte carlo ii an improved proof using soft markov chain decomposition"):
  with members:
    - "beyond log concavity provable guarantees for sampling multi modal distributions using simulated tempering langevin monte carlo"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "escaping the local minima via simulated annealing optimization of approximately convex functions"
    - "partitioning into expanders"

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)