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 "estimating normalizing constants for log concave distributions algorithms and lower bounds"):
  Abstract: Estimating the normalizing constant of an unnormalized probability distribution has important applications in computer science, statistical physics, machine learning, and statistics. In this work, we consider the problem of estimating the normalizing constant $Z=\int_{\mathbb{R}^d} e^{-f(x)}\,\mathrm{d}x$ to within a multiplication factor of $1 \pm \varepsilon$ for a $\mu$-strongly convex and $L$-smooth function $f$, given query access to $f(x)$ and $\nabla f(x)$. We give both algorithms and lowerbounds for this problem. Using an annealing algorithm combined with a multilevel Monte Carlo method based on underdamped Langevin dynamics, we show that $\widetilde{\mathcal{O}}\Bigl(\frac{d^{4/3}\kappa + d^{7/6}\kappa^{7/6}}{\varepsilon^2}\Bigr)$ queries to $\nabla f$ are sufficient, where $\kappa= L / \mu$ is the condition number. Moreover, we provide an information theoretic lowerbound, showing that at least $\frac{d^{1-o(1)}}{\varepsilon^{2-o(1)}}$ queries are necessary. This provides a first nontrivial lowerbound for the problem.

Includes members:
  Member "the randomized midpoint method for log concave sampling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "logsmooth gradient concentration and tighter runtimes for metropolized hamiltonian monte carlo")
  Member "optimal convergence rate of hamiltonian monte carlo for strongly logconcave distributions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "logsmooth gradient concentration and tighter runtimes for metropolized hamiltonian monte carlo")
  Member "rapid convergence of the unadjusted langevin algorithm log sobolev suffices":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal convergence rate of hamiltonian monte carlo for strongly logconcave distributions")
  Member "algorithmic theory of odes and sampling from well conditioned logconcave densities":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "logsmooth gradient concentration and tighter runtimes for metropolized hamiltonian monte carlo")
  Member "dimensionally tight bounds for second order hamiltonian monte carlo":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rapid convergence of the unadjusted langevin algorithm isoperimetry suffices")
  Member "geodesic walks in polytopes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the approximability of weighted model integration on dnf structures")
  Member "gaussian cooling and o n 3 algorithms for volume and gaussian volume":
    also connected through other hyperedges:
      (none)
  Member "dispersion of mass and the complexity of randomized geometric algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "query complexity of sampling and small geometric partitions")

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)