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: "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"
  Abstract: Algorithmic stability is a classical approach to understanding and analysis of the generalization error of learning algorithms. A notable weakness of most stability-based generalization bounds is that they hold only in expectation. Generalization with high probability has been established in a landmark paper of Bousquet and Elisseeff (2002) albeit at the expense of an additional $\sqrt{n}$ factor in the bound. Specifically, their bound on the estimation error of any $\gamma$-uniformly stable learning algorithm on $n$ samples and range in $[0,1]$ is $O(\gamma \sqrt{n \log(1/\delta)} + \sqrt{\log(1/\delta)/n})$ with probability $\geq 1-\delta$. The $\sqrt{n}$ overhead makes the bound vacuous in the common settings where $\gamma \geq 1/\sqrt{n}$. A stronger bound was recently proved by the authors (Feldman and Vondrak, 2018) that reduces the overhead to at most $O(n^{1/4})$. Still, both of these results give optimal generalization bounds only when $\gamma = O(1/n)$. #R##N#We prove a nearly tight bound of $O(\gamma \log(n)\log(n/\delta) + \sqrt{\log(1/\delta)/n})$ on the estimation error of any $\gamma$-uniformly stable algorithm. It implies that algorithms that are uniformly stable with $\gamma = O(1/\sqrt{n})$ have essentially the same estimation error as algorithms that output a fixed function. Our result leads to the first high-probability generalization bounds for multi-pass stochastic gradient descent and regularized ERM for stochastic convex problems with nearly optimal rate --- resolving open problems in prior work. Our proof technique is new and we introduce several analysis tools that might find additional applications.

Connected through hyperedge HE-1 (induced by source paper "reasoning about generalization via conditional mutual information"):
  with members:
    - "robust generalization via α mutual information"
    - "in defense of uniform convergence generalization via derandomization with an application to interpolating predictors"
    - "strengthened information theoretic bounds on the generalization error"
    - "tightening mutual information based bounds on generalization error"
    - "a new analysis of differential privacy s generalization guarantees"
    - "pac learning with stable and private predictions"
    - "a direct sum result for the information complexity of learning"
    - "chaining mutual information and tightening generalization bounds"

Connected through hyperedge HE-2 (induced by source paper "does learning require memorization a short tale about a long tail"):
  with members:
    - "learning imbalanced datasets with label distribution aware margin loss"
    - "two models of double descent for weak features"
    - "harmless interpolation of noisy data in regression"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "towards demystifying membership inference attacks"
    - "dnn or k nn that is the generalize vs memorize question"
    - "privacy preserving prediction"
    - "understanding membership inferences on well generalized learning models"

Connected through hyperedge HE-3 (induced by source paper "on generalization error bounds of noisy gradient methods for non convex learning"):
  with members:
    - "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"
    - "local optimality and generalization guarantees for the langevin algorithm via empirical metastability"

Connected through hyperedge HE-4 (induced by source paper "pac learning with stable and private predictions"):
  with members:
    - "privately answering classification queries in the agnostic pac model"
    - "towards demystifying membership inference attacks"
    - "model agnostic private learning via stability"
    - "private pac learning implies finite littlestone dimension"
    - "privacy preserving prediction"
    - "towards measuring membership privacy"
    - "subgaussian tail bounds via stability arguments"
    - "learning privately from multiparty data"

Connected through hyperedge HE-5 (induced by source paper "private stochastic convex optimization with optimal rates"):
  with members:
    - "differentially private empirical risk minimization revisited faster and more general"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "generalization of erm in stochastic convex optimization the dimension strikes back"
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"
    - "train faster generalize better stability of stochastic gradient descent"
    - "algorithmic stability for adaptive data analysis"
    - "preserving statistical validity in adaptive data analysis"
    - "differentially private empirical risk minimization efficient algorithms and tight error bounds"

Connected through hyperedge HE-6 (induced by source paper "implicit regularization for deep neural networks driven by an ornstein uhlenbeck like process"):
  with members:
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "data dependent stability of stochastic gradient descent"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "sgd learns over parameterized networks that provably generalize on linearly separable data"
    - "algorithmic regularization in over parameterized matrix sensing and neural networks with quadratic activations"
    - "understanding deep learning requires rethinking generalization"
    - "train faster generalize better stability of stochastic gradient descent"
    - "adding noise to the input of a model trained with a regularized objective"

Connected through hyperedge HE-7 (induced by source paper "sharper bounds for uniformly stable algorithms"):
  with members:
    - "generalization bounds for uniformly stable algorithms"

Connected through hyperedge HE-8 (induced by source paper "generalization bounds for uniformly stable algorithms"):
  with members:
    - "privacy preserving prediction"
    - "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"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "generalization of erm in stochastic convex optimization the dimension strikes back"
    - "on the generalization properties of differential privacy"

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)