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: "optimistic rates for learning with a smooth loss"
  Abstract: We establish an excess risk bound of O(H R_n^2 + R_n \sqrt{H L*}) for empirical risk minimization with an H-smooth loss function and a hypothesis class with Rademacher complexity R_n, where L* is the best risk achievable by the hypothesis class. For typical hypothesis classes where R_n = \sqrt{R/n}, this translates to a learning rate of O(RH/n) in the separable (L*=0) case and O(RH/n + \sqrt{L^* RH/n}) more generally. We also provide similar guarantees for online and stochastic convex optimization with a smooth non-negative objective.

Connected through hyperedge HE-1 (induced by source paper "theoretical analysis of divide and conquer erm beyond square loss and rkhs"):
  with members:
    - "empirical risk minimization for stochastic convex optimization o 1 n and o 1 n 2 type of risk bounds"
    - "divide and conquer local average regression"
    - "distributed learning with regularized least squares"
    - "refined error bounds for several learning algorithms"
    - "distributed autonomous online learning regrets and intrinsic privacy preserving properties"

Connected through hyperedge HE-2 (induced by source paper "dropout explicit forms and capacity control"):
  with members:
    - "on dropout and nuclear norm regularization"
    - "on the implicit bias of dropout"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "on the relationship between dropout and equiangular tight frames"
    - "brain tumor segmentation with deep neural networks"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"
    - "spectrally normalized margin bounds for neural networks"
    - "size independent sample complexity of neural networks"

Connected through hyperedge HE-3 (induced by source paper "a modern introduction to online learning"):
  with members:
    - "on the convergence of adam and beyond"
    - "black box reductions for parameter free online learning in banach spaces"
    - "a modular analysis of adaptive non convex optimization optimism composite objectives and variational bounds"
    - "training deep networks without learning rates through coin betting"
    - "scale free online learning"
    - "coin betting and parameter free online learning"
    - "fighting bandits with a new kind of smoothness"
    - "second order quantile methods for experts and combinatorial games"

Connected through hyperedge HE-4 (induced by source paper "improved sample complexities for deep networks and robust classification via an all layer margin"):
  with members:
    - "a hessian based complexity measure for deep networks"
    - "learning imbalanced datasets with label distribution aware margin loss"
    - "vc classes are adversarially robustly learnable but only improperly"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "theoretically principled trade off between robustness and accuracy"
    - "gradient descent maximizes the margin of homogeneous neural networks"
    - "adversarial training can hurt generalization"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"

Connected through hyperedge HE-5 (induced by source paper "stochastic approximation of smooth and strongly convex functions beyond the o 1 t convergence rate"):
  with members:
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "empirical risk minimization for stochastic convex optimization o 1 n and o 1 n 2 type of risk bounds"
    - "memory and communication efficient distributed stochastic optimization with minibatch prox"
    - "from exp concavity to variance control high probability o 1 n rates and high probability online to batch conversion"
    - "generalization of erm in stochastic convex optimization the dimension strikes back"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "passive learning with target risk"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"

Connected through hyperedge HE-6 (induced by source paper "everything old is new again a multi view learning approach to learning using privileged information and distillation"):
  with members:
    - "stochastic canonical correlation analysis"
    - "from captions to visual concepts and back"
    - "multilingual distributed representations without word alignment"
    - "do deep nets really need to be deep"

Connected through hyperedge HE-7 (induced by source paper "decoupling gating from linearity"):
  with members:
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a comparative analysis of the optimization and generalization property of two layer neural network and random feature models under gradient descent dynamics"
    - "on the connection between learning two layers neural networks and tensor decomposition"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "overparameterized nonlinear learning gradient descent takes the shortest path"
    - "the global optimization geometry of shallow linear neural networks"
    - "a convergence analysis of gradient descent for deep linear neural networks"

Connected through hyperedge HE-8 (induced by source paper "max diversity distributed learning theory and algorithms"):
  with members:
    - "empirical risk minimization for stochastic convex optimization o 1 n and o 1 n 2 type of risk bounds"
    - "distributed learning with regularized least squares"

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)