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: "online sampling from log concave distributions"
  Abstract: Given a sequence of convex functions $f_0, f_1, \ldots, f_T$, we study the problem of sampling from the Gibbs distribution $\pi_t \propto e^{-\sum_{k=0}^t f_k}$ for each epoch $t$ in an online manner. This problem occurs in applications to machine learning, Bayesian statistics, and optimization where one constantly acquires new data, and must continuously update the distribution. Our main result is an algorithm that generates independent samples from a distribution that is a fixed $\varepsilon$ TV-distance from $\pi_t$ for every $t$ and, under mild assumptions on the functions, makes poly$\log(T)$ gradient evaluations per epoch. All previous results for this problem imply a bound on the number of gradient or function evaluations which is at least linear in $T$. While we assume the functions have bounded second moment, we do not assume strong convexity. In particular, we show that our assumptions hold for online Bayesian logistic regression, when the data satisfy natural regularity properties. In simulations, our algorithm achieves accuracy comparable to that of a Markov chain specialized to logistic regression. Our main result also implies the first algorithm to sample from a $d$-dimensional log-concave distribution $\pi_T \propto e^{-\sum_{k=0}^T f_k}$ where the $f_k$'s are not assumed to be strongly convex and the total number of gradient evaluations is roughly $T\log(T)+\mathrm{poly}(d),$ as opposed to $T\cdot \mathrm{poly}(d)$ implied by prior works. Key to our algorithm is a novel stochastic gradient Langevin dynamics Markov chain that has a carefully designed variance reduction step built-in with fixed constant batch size. Technically, lack of strong convexity is a significant barrier to the analysis, and, here, our main contribution is a martingale exit time argument showing the chain is constrained to a ball of radius roughly poly$\log(T)$ for the duration of the algorithm.

Connected through hyperedge HE-1 (induced by source paper "nonconvex sampling with the metropolis adjusted langevin algorithm"):
  with members:
    - "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"
    - "convergence rate of riemannian hamiltonian monte carlo and faster polytope volume computation"
    - "efficient learning of linear separators under bounded noise"

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.LG(Machine Learning)