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: "local optimality and generalization guarantees for the langevin algorithm via empirical metastability"
  Abstract: We study the detailed path-wise behavior of the discrete-time Langevin algorithm for non-convex Empirical Risk Minimization (ERM) through the lens of metastability, adopting some techniques from Berglund and Gentz. #R##N#For a particular local optimum of the empirical risk, with an arbitrary initialization, we show that, with high probability, one of the two mutually exclusive events will occur: either the Langevin trajectory ends up somewhere outside the $\varepsilon$-neighborhood of this particular optimum within a short recurrence time; or it enters this $\varepsilon$-neighborhood by the recurrence time and stays there until an exponentially long escape time. We call this phenomenon empirical metastability. #R##N#This two-timescale characterization aligns nicely with the existing literature in the following two senses. First, the recurrence time is dimension-independent, and resembles the convergence time of deterministic Gradient Descent (GD). However unlike GD, the Langevin algorithm does not require strong conditions on local initialization, and has the possibility of eventually visiting all optima. Second, the scaling of the escape time is consistent with the Eyring-Kramers law, which states that the Langevin scheme will eventually visit all local minima, but it will take an exponentially long time to transit among them. We apply this path-wise concentration result in the context of statistical learning to examine local notions of generalization and optimality.

Connected through hyperedge HE-1 (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-2 (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"
    - "exploring generalization in deep learning"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "distribution dependent analysis of gibbs erm principle"):
  with members:
    - "iterate averaging as regularization for stochastic gradient descent"
    - "data dependent pac bayes priors via differential privacy"
    - "piecewise strong convexity of neural networks"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "fisher rao metric geometry and complexity of neural networks"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "generalization bounds of sgld for non convex learning two theoretical viewpoints"
    - "a tight excess risk bound via a unified pac bayesian rademacher shtarkov mdl complexity"

Connected through hyperedge HE-4 (induced by source paper "bayesian pose graph optimization via bingham distributions and tempered geodesic mcmc"):
  with members:
    - "convex relaxations for pose graph optimization with outliers"
    - "rotation averaging and strong duality"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "cad priors for accurate and flexible instance reconstruction"
    - "camera pose filtering with local regression geodesics on the riemannian manifold of dual quaternions"
    - "a certifiably correct algorithm for synchronization over the special euclidean group"
    - "spectral synchronization of multiple views in se 3"

Connected through hyperedge HE-5 (induced by source paper "information theoretic interpretation of deep learning"):
  with members:
    - "exploring generalization in deep learning"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "spectrally normalized margin bounds for neural networks"
    - "opening the black box of deep neural networks via information"
    - "deep variational information bottleneck"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)