human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "dimensionally tight running time bounds for second order hamiltonian monte carlo"):
  Abstract: Hamiltonian Monte Carlo (HMC) is a widely deployed method to sample from a given high-dimensional distribution in Statistics and Machine learning. HMC is known to run very efficiently in practice and its second-order variant was conjectured to run in $d^{1/4}$ steps in 1988. Here we show that this conjecture is true when sampling from strongly log-concave target distributions that satisfy weak third-order regularity properties associated with the input data. This improves upon a recent result that shows that the number of steps of the second-order discretization of HMC grows like $d^{1/4}$ under the much stronger assumption that the distribution is separable and its first four Fr\'echet derivatives are bounded. Our result also compares favorably with the best available running time bounds for the class of strongly log-concave distributions, namely the current best bounds for both the overdamped and underdamped Langevin, and first-order HMC Algorithms, which all grow like $d^{{1}/{2}}$ with the dimension. Key to our result is a new regularity condition for the Hessian that may be of independent interest. The class of distributions that satisfy this condition are natural and include posterior distributions used in Bayesian logistic "ridge" regression.

Includes members:
  Member "convergence rate of riemannian hamiltonian monte carlo and faster polytope volume computation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "strong self concordance and sampling")
  Member "matrix scaling and balancing via box constrained newton s method and interior point methods":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "flows in almost linear time via adaptive preconditioning")

Question: Please tell me which class the source paper 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)