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: "generalization bounds for uniformly stable algorithms"
  Abstract: Uniform stability of a learning algorithm is a classical notion of algorithmic stability introduced to derive high-probability bounds on the generalization error (Bousquet and Elisseeff, 2002). Specifically, for a loss function with range bounded in $[0,1]$, the generalization error of a $\gamma$-uniformly stable learning algorithm on $n$ samples is known to be within $O((\gamma +1/n) \sqrt{n \log(1/\delta)})$ of the empirical error with probability at least $1-\delta$. Unfortunately, this bound does not lead to meaningful generalization bounds in many common settings where $\gamma \geq 1/\sqrt{n}$. At the same time the bound is known to be tight only when $\gamma = O(1/n)$. #R##N#We substantially improve generalization bounds for uniformly stable algorithms without making any additional assumptions. First, we show that the bound in this setting is $O(\sqrt{(\gamma + 1/n) \log(1/\delta)})$ with probability at least $1-\delta$. In addition, we prove a tight bound of $O(\gamma^2 + 1/n)$ on the second moment of the estimation error. The best previous bound on the second moment is $O(\gamma + 1/n)$. Our proofs are based on new analysis techniques and our results imply substantially stronger generalization guarantees for several well-studied algorithms.

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"
    - "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "a rigorous framework for the mean field limit of multilayer neural networks"):
  with members:
    - "landscape connectivity and dropout stability of sgd solutions for over parameterized neural networks"
    - "mean field limit of the learning dynamics of multilayer neural networks"
    - "gradient descent finds global minima of deep neural networks"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"
    - "a convergence theory for deep learning via over parameterization"
    - "convergence analysis of two layer neural networks with relu activation"

Connected through hyperedge HE-3 (induced by source paper "stable and fair classification"):
  with members:
    - "toward controlling discrimination in online ad auctions"
    - "fair and diverse dpp based data summarization"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "classification with fairness constraints a meta algorithm with provable guarantees"
    - "a reductions approach to fair classification"
    - "on fairness and calibration"
    - "certified defenses for data poisoning attacks"
    - "mathematics of deep learning"

Connected through hyperedge HE-4 (induced by source paper "a new analysis of differential privacy s generalization guarantees"):
  with members:
    - "a necessary and sufficient stability notion for adaptive generalization"
    - "the advantages of multiple classes for reducing overfitting from test set reuse"
    - "guaranteed validity for empirical approaches to adaptive data analysis"
    - "mitigating bias in adaptive data gathering via differential privacy"
    - "generalization for adaptively chosen estimators via stable median"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "calibrating noise to variance in adaptive data analysis"
    - "subgaussian tail bounds via stability arguments"

Connected through hyperedge HE-5 (induced by source paper "uniform convergence may be unable to explain generalization in deep learning"):
  with members:
    - "generalization in deep networks the role of distance from initialization"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"

Connected through hyperedge HE-6 (induced by source paper "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"):
  with members:
    - "hypothesis set stability and generalization"
    - "privacy preserving prediction"
    - "tight analyses for non smooth stochastic gradient descent"
    - "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-7 (induced by source paper "hypothesis set stability and generalization"):
  with members:
    - "subgaussian tail bounds via stability arguments"
    - "algorithmic stability for adaptive data analysis"
    - "almost everywhere algorithmic stability and generalization error"
    - "transductive rademacher complexity and its applications"

Connected through hyperedge HE-8 (induced by source paper "sharper bounds for uniformly stable algorithms"):
  with members:
    - "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"

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)