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: "escaping the local minima via simulated annealing optimization of approximately convex functions"
  Abstract: We consider the problem of optimizing an approximately convex function over a bounded convex set in $\mathbb{R}^n$ using only function evaluations. The problem is reduced to sampling from an \emph{approximately} log-concave distribution using the Hit-and-Run method, which is shown to have the same $\mathcal{O}^*$ complexity as sampling from log-concave distributions. In addition to extend the analysis for log-concave distributions to approximate log-concave distributions, the implementation of the 1-dimensional sampler of the Hit-and-Run walk requires new methods and analysis. The algorithm then is based on simulated annealing which does not relies on first order conditions which makes it essentially immune to local minima. #R##N#We then apply the method to different motivating problems. In the context of zeroth order stochastic convex optimization, the proposed method produces an $\epsilon$-minimizer after $\mathcal{O}^*(n^{7.5}\epsilon^{-2})$ noisy function evaluations by inducing a $\mathcal{O}(\epsilon/n)$-approximately log concave distribution. We also consider in detail the case when the "amount of non-convexity" decays towards the optimum of the function. Other applications of the method discussed in this work include private computation of empirical risk minimizers, two-stage stochastic programming, and approximate dynamic programming for online learning.

Connected through hyperedge HE-1 (induced by source paper "simulated tempering langevin monte carlo ii an improved proof using soft markov chain decomposition"):
  with members:
    - "rapid convergence of the unadjusted langevin algorithm log sobolev suffices"
    - "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"
    - "partitioning into expanders"

Connected through hyperedge HE-2 (induced by source paper "beyond log concavity provable guarantees for sampling multi modal distributions using simulated tempering langevin monte carlo"):
  with members:
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "provable learning of noisy or networks"
    - "simple efficient and neural algorithms for sparse coding"
    - "on some provably correct cases of variational inference for topic models"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "a spectral algorithm for latent dirichlet allocation"
    - "learning topic models going beyond svd"

Connected through hyperedge HE-3 (induced by source paper "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"):
  with members:
    - "on graduated optimization for stochastic non convex problems"
    - "wasserstein continuity of entropy and outer bounds for interference channels"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"

Connected through hyperedge HE-4 (induced by source paper "generalization of erm in stochastic convex optimization the dimension strikes back"):
  with members:
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"
    - "generalization in adaptive data analysis and holdout reuse"
    - "train faster generalize better stability of stochastic gradient descent"
    - "algorithmic stability for adaptive data analysis"
    - "sequential probability assignment with binary alphabets and large classes of experts"
    - "preserving statistical validity in adaptive data analysis"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"

Connected through hyperedge HE-5 (induced by source paper "kernel based methods for bandit convex optimization"):
  with members:
    - "an optimal algorithm for bandit convex optimization"
    - "matching the universal barrier without paying the costs solving linear programs with o sqrt rank linear system solves"
    - "towards minimax policies for online linear optimization with bandit feedback"
    - "on the complexity of bandit and derivative free stochastic convex optimization"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-6 (induced by source paper "tolerant junta testing and the connection to submodular optimization and function isomorphism"):
  with members:
    - "lower bounds for tolerant junta and unateness testing via rejection sampling of graphs"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "tolerant testers of image properties"
    - "local reconstructors and tolerant testers for connectivity and diameter"
    - "learning with submodular functions a convex optimization perspective"
    - "submodular approximation sampling based algorithms and lower bounds"

Connected through hyperedge HE-7 (induced by source paper "approximate modularity revisited"):
  with members:
    - "submodular optimization under noise"
    - "when are welfare guarantees robust"
    - "the limitations of optimization from samples"
    - "combinatorial auctions with decreasing marginal utilities"

Connected through hyperedge HE-8 (induced by source paper "statistical query algorithms for stochastic convex optimization"):
  with members:
    - "generalization in adaptive data analysis and holdout reuse"
    - "algorithmic stability for adaptive data analysis"
    - "sum of squares lower bounds for planted clique"
    - "preventing false discovery in interactive data analysis is hard"
    - "lower bounds on the size of semidefinite programming relaxations"
    - "preserving statistical validity in adaptive data analysis"
    - "private multiplicative weights beyond linear queries"
    - "interactive fingerprinting codes and the hardness of preventing false discovery"

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.NA(Numerical Analysis)