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: "model agnostic private learning via stability"
  Abstract: We design differentially private learning algorithms that are agnostic to the learning model. Our algorithms are interactive in nature, i.e., instead of outputting a model based on the training data, they provide predictions for a set of $m$ feature vectors that arrive online. We show that, for the feature vectors on which an ensemble of models (trained on random disjoint subsets of a dataset) makes consistent predictions, there is almost no-cost of privacy in generating accurate predictions for those feature vectors. To that end, we provide a novel coupling of the distance to instability framework with the sparse vector technique. We provide algorithms with formal privacy and utility guarantees for both binary/multi-class classification, and soft-label classification. For binary classification in the standard (agnostic) PAC model, we show how to bootstrap from our privately generated predictions to construct a computationally efficient private learner that outputs a final accurate hypothesis. Our construction - to the best of our knowledge - is the first computationally efficient construction for a label-private learner. We prove sample complexity upper bounds for this setting. As in non-private sample complexity bounds, the only relevant property of the given concept class is its VC dimension. For soft-label classification, our techniques are based on exploiting the stability properties of traditional learning algorithms, like stochastic gradient descent (SGD). We provide a new technique to boost the average-case stability properties of learning algorithms to strong (worst-case) stability properties, and then exploit them to obtain private classification algorithms. In the process, we also show that a large class of SGD methods satisfy average-case stability properties, in contrast to a smaller class of SGD methods that are uniformly stable as shown in prior work.

Connected through hyperedge HE-1 (induced by source paper "pac learning with stable and private predictions"):
  with members:
    - "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"
    - "privately answering classification queries in the agnostic pac model"
    - "towards demystifying membership inference attacks"
    - "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-2 (induced by source paper "how to use heuristics for differential privacy"):
  with members:
    - "unleashing linear optimizers for group fair learning and optimization"
    - "property testing for differential privacy"
    - "private pac learning implies finite littlestone dimension"
    - "privacy preserving prediction"
    - "online learning via differential privacy"
    - "adaptive learning with robust generalization guarantees"
    - "efficient algorithms for adversarial contextual learning"
    - "differentially private release and learning of threshold functions"

Connected through hyperedge HE-3 (induced by source paper "private pac learning implies finite littlestone dimension"):
  with members:
    - "privacy preserving prediction"
    - "adaptive learning with robust generalization guarantees"
    - "simultaneous private learning of multiple concepts"
    - "differentially private release and learning of threshold functions"
    - "algorithmic stability for adaptive data analysis"
    - "the large margin mechanism for differentially private maximization"
    - "characterizing the sample complexity of private learners"
    - "sample complexity bounds on differentially private learning via communication complexity"

Connected through hyperedge HE-4 (induced by source paper "privacy preserving prediction"):
  with members:
    - "differentially private empirical risk minimization revisited faster and more general"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "generalization for adaptively chosen estimators via stable median"
    - "learning privately from multiparty data"
    - "train faster generalize better stability of stochastic gradient descent"
    - "algorithmic stability for adaptive data analysis"
    - "characterizing the sample complexity of private learners"
    - "differentially private empirical risk minimization"

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)