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: "a hitting time analysis of stochastic gradient langevin dynamics"
  Abstract: We study the Stochastic Gradient Langevin Dynamics (SGLD) algorithm for non-convex optimization. The algorithm performs stochastic gradient descent, where in each step it injects appropriately scaled Gaussian noise to the update. We analyze the algorithm's hitting time to an arbitrary subset of the parameter space. Two results follow from our general theory: First, we prove that for empirical risk minimization, if the empirical risk is point-wise close to the (smooth) population risk, then the algorithm achieves an approximate local minimum of the population risk in polynomial time, escaping suboptimal local minima that only exist in the empirical risk. Second, we show that SGLD improves on one of the best known learnability results for learning linear classifiers under the zero-one loss.

Connected through hyperedge HE-1 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise"):
  with members:
    - "noise tolerant reliable active classification with comparison queries"
    - "a modern introduction to online learning"
    - "adaptive hard thresholding for near optimal consistent robust regression"
    - "distribution independent pac learning of halfspaces with massart noise"
    - "nearly tight bounds for robust proper learning of halfspaces with a margin"
    - "efficient active learning of sparse halfspaces"
    - "active classification with comparison queries"
    - "revisiting perceptron efficient and label optimal learning of halfspaces"

Connected through hyperedge HE-2 (induced by source paper "learning halfspaces with massart noise under structured distributions"):
  with members:
    - "nonconvex sampling with the metropolis adjusted langevin algorithm"
    - "the complexity of finding stationary points with stochastic gradient descent"
    - "distribution independent pac learning of halfspaces with massart noise"
    - "learning geometric concepts with nasty noise"
    - "revisiting perceptron efficient and label optimal learning of halfspaces"
    - "efficient learning of linear separators under bounded noise"
    - "complexity theoretic limitations on learning halfspaces"
    - "analysis of boolean functions"

Connected through hyperedge HE-3 (induced by source paper "on generalization error bounds of noisy gradient methods for non convex learning"):
  with members:
    - "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"
    - "on exact computation with an infinitely wide neural net"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "generalization error bounds for noisy iterative algorithms"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"

Connected through hyperedge HE-4 (induced by source paper "the multiplicative noise in stochastic gradient descent data dependent regularization continuous and discrete approximation"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "uniform in time weak error analysis for stochastic gradient descent algorithms via diffusion approximation"
    - "an empirical study on regularization of deep neural networks by local rademacher complexity"
    - "quasi potential as an implicit regularizer for the loss function in the stochastic gradient descent"
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"
    - "three factors influencing minima in sgd"
    - "an alternative view when does sgd escape local minima"
    - "escaping saddles with stochastic gradients"

Connected through hyperedge HE-5 (induced by source paper "on nonconvex optimization for machine learning gradients stochasticity and saddle points"):
  with members:
    - "finding local minima via stochastic nested variance reduction"
    - "escaping saddles with stochastic gradients"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "stochastic cubic regularization for fast nonconvex optimization"
    - "neon2 finding local minima via first order oracles"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "a generic approach for escaping saddle points"
    - "how to escape saddle points efficiently"

Connected through hyperedge HE-6 (induced by source paper "cyclical stochastic gradient mcmc for bayesian deep learning"):
  with members:
    - "averaging weights leads to wider optima and better generalization"
    - "snapshot ensembles train 1 get m for free"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "super convergence very fast training of residual networks using large learning rates"
    - "bayesian recurrent neural networks"
    - "sgdr stochastic gradient descent with restarts"
    - "plug play generative networks conditional iterative generation of images in latent space"
    - "an efficient minibatch acceptance test for metropolis hastings"

Connected through hyperedge HE-7 (induced by source paper "distribution independent pac learning of halfspaces with massart noise"):
  with members:
    - "efficient algorithms and lower bounds for robust linear regression"
    - "efficient algorithms for outlier robust regression"
    - "sever a robust meta algorithm for stochastic optimization"
    - "learning geometric concepts with nasty noise"
    - "being robust in high dimensions can be practical"
    - "robustly learning a gaussian getting optimal error efficiently"
    - "revisiting perceptron efficient and label optimal learning of halfspaces"
    - "agnostic estimation of mean and covariance"

Connected through hyperedge HE-8 (induced by source paper "a tail index analysis of stochastic gradient noise in deep neural networks"):
  with members:
    - "three factors influencing minima in sgd"
    - "revisiting small batch training for deep neural networks"
    - "local optimality and generalization guarantees for the langevin algorithm via empirical metastability"
    - "exploring generalization in deep learning"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "don t decay the learning rate increase the batch size"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "empirical analysis of the hessian of over parametrized neural networks"

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)