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: "tight analyses for non smooth stochastic gradient descent"
  Abstract: Consider the problem of minimizing functions that are Lipschitz and strongly convex, but not necessarily differentiable. We prove that after $T$ steps of stochastic gradient descent, the error of the final iterate is $O(\log(T)/T)$ with high probability. We also construct a function from this class for which the error of the final iterate of deterministic gradient descent is $\Omega(\log(T)/T)$. This shows that the upper bound is tight and that, in this setting, the last iterate of stochastic gradient descent has the same general error rate (with high probability) as deterministic gradient descent. This resolves both open questions posed by Shamir (2012). #R##N#An intermediate step of our analysis proves that the suffix averaging method achieves error $O(1/T)$ with high probability, which is optimal (for any first-order optimization method). This improves results of Rakhlin (2012) and Hazan and Kale (2014), both of which achieved error $O(1/T)$, but only in expectation, and achieved a high probability error bound of $O(\log \log(T)/T)$, which is suboptimal. #R##N#We prove analogous results for functions that are Lipschitz and convex, but not necessarily strongly convex or differentiable. After $T$ steps of stochastic gradient descent, the error of the final iterate is $O(\log(T)/\sqrt{T})$ with high probability, and there exists a function for which the error of the final iterate of deterministic gradient descent is $\Omega(\log(T)/\sqrt{T})$.

Connected through hyperedge HE-1 (induced by source paper "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"):
  with members:
    - "hypothesis set stability and generalization"
    - "generalization bounds for uniformly stable algorithms"
    - "privacy preserving prediction"
    - "statistical optimality of stochastic gradient descent on hard learning problems through multiple passes"
    - "data dependent stability of stochastic gradient descent"
    - "subgaussian tail bounds via stability arguments"
    - "concentration bounds for high sensitivity functions through differential privacy"
    - "a pac bayesian analysis of randomized learning with application to stochastic gradient descent"

Connected through hyperedge HE-2 (induced by source paper "the step decay schedule a near optimal geometrically decaying learning rate procedure for least squares"):
  with members:
    - "iterate averaging as regularization for stochastic gradient descent"
    - "how to make the gradients small stochastically"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "identity mappings in deep residual networks"
    - "deep residual learning for image recognition"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"
    - "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"

Connected through hyperedge HE-3 (induced by source paper "the step decay schedule a near optimal geometrically decaying learning rate procedure"):
  with members:
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "how to make the gradients small stochastically"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "identity mappings in deep residual networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"
    - "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"

Connected through hyperedge HE-4 (induced by source paper "simple and optimal high probability bounds for strongly convex stochastic gradient descent"):
  with members:
    - "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"
    - "making gradient descent optimal for strongly convex stochastic optimization"
    - "on the number of iterations for dantzig wolfe optimization and packing covering approximation algorithms"

Connected through hyperedge HE-5 (induced by source paper "smooth structured prediction using quantum and classical gibbs samplers"):
  with members:
    - "free energy based reinforcement learning using a quantum processor"
    - "reward augmented maximum likelihood for neural structured prediction"
    - "adam a method for stochastic optimization"
    - "learning deep structured models"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"
    - "block coordinate frank wolfe optimization for structural svms"
    - "making gradient descent optimal for strongly convex stochastic optimization"

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)