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: "learning privately with labeled and unlabeled examples"
  Abstract: A private learner is an algorithm that given a sample of labeled individual examples outputs a generalizing hypothesis while preserving the privacy of each individual. In 2008, Kasiviswanathan et al. (FOCS 2008) gave a generic construction of private learners, in which the sample complexity is (generally) higher than what is needed for non-private learners. This gap in the sample complexity was then further studied in several followup papers, showing that (at least in some cases) this gap is unavoidable. Moreover, those papers considered ways to overcome the gap, by relaxing either the privacy or the learning guarantees of the learner. #R##N#We suggest an alternative approach, inspired by the (non-private) models of semi-supervised learning and active-learning, where the focus is on the sample complexity of labeled examples whereas unlabeled examples are of a significantly lower cost. We consider private semi-supervised learners that operate on a random sample, where only a (hopefully small) portion of this sample is labeled. The learners have no control over which of the sample elements are labeled. Our main result is that the labeled sample complexity of private learners is characterized by the VC dimension. #R##N#We present two generic constructions of private semi-supervised learners. The first construction is of learners where the labeled sample complexity is proportional to the VC dimension of the concept class, however, the unlabeled sample complexity of the algorithm is as big as the representation length of domain elements. Our second construction presents a new technique for decreasing the labeled sample complexity of a given private learner, while roughly maintaining its unlabeled sample complexity. In addition, we show that in some settings the labeled sample complexity does not depend on the privacy parameters of the learner.

Connected through hyperedge HE-1 (induced by source paper "an equivalence between private classification and online prediction"):
  with members:
    - "efficient noise tolerant and private learning via boosting"
    - "a necessary and sufficient stability notion for adaptive generalization"
    - "passing tests without memorizing two models for fooling discriminators"
    - "introduction to online convex optimization"
    - "privately learning thresholds closing the exponential gap"
    - "privately answering classification queries in the agnostic pac model"
    - "the role of interactivity in local differential privacy"
    - "private learning implies online learning an efficient reduction"

Connected through hyperedge HE-2 (induced by source paper "closure properties for private classification and online prediction"):
  with members:
    - "a new analysis of differential privacy s generalization guarantees"
    - "privately learning thresholds closing the exponential gap"
    - "the role of interactivity in local differential privacy"
    - "private learning implies online learning an efficient reduction"
    - "private pac learning implies finite littlestone dimension"
    - "generalization for adaptively chosen estimators via stable median"
    - "online learning via differential privacy"
    - "differentially private release and learning of threshold functions"

Connected through hyperedge HE-3 (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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "private center points and learning of halfspaces"):
  with members:
    - "privacy preserving prediction"
    - "simultaneous private learning of multiple concepts"
    - "order revealing encryption and the hardness of private learning"
    - "differentially private release and learning of threshold functions"
    - "characterizing the sample complexity of private learners"
    - "sample complexity bounds on differentially private learning via communication complexity"
    - "private learning and sanitization pure vs approximate differential privacy"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-5 (induced by source paper "differentially private learning of geometric concepts"):
  with members:
    - "privacy preserving prediction"
    - "simultaneous private learning of multiple concepts"
    - "order revealing encryption and the hardness of private learning"
    - "differentially private release and learning of threshold functions"
    - "characterizing the sample complexity of private learners"
    - "sample complexity bounds on differentially private learning via communication complexity"
    - "private learning and sanitization pure vs approximate differential privacy"
    - "differentially private combinatorial optimization"

Connected through hyperedge HE-6 (induced by source paper "privately answering classification queries in the agnostic pac model"):
  with members:
    - "pac learning with stable and private predictions"
    - "private pac learning implies finite littlestone dimension"
    - "privacy preserving prediction"
    - "learning privately from multiparty data"
    - "differentially private release and learning of threshold functions"
    - "calibrating data to sensitivity in private data analysis"
    - "on sampling anonymization and differential privacy or k anonymization meets differential privacy"

Connected through hyperedge HE-7 (induced by source paper "limits of private learning with access to public data"):
  with members:
    - "private pac learning implies finite littlestone dimension"
    - "learning privately from multiparty data"
    - "differentially private release and learning of threshold functions"
    - "private learning and sanitization pure vs approximate differential privacy"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-8 (induced by source paper "private pac learning implies finite littlestone dimension"):
  with members:
    - "model agnostic private learning via stability"
    - "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"

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)