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: "what can we learn privately"
  Abstract: Learning problems form an important category of computational tasks that generalizes many of the computations researchers apply to large real-life data sets. We ask: what concept classes can be learned privately, namely, by an algorithm whose output does not depend too heavily on any one input or specific training example? More precisely, we investigate learning algorithms that satisfy differential privacy, a notion that provides strong confidentiality guarantees in contexts where aggregate information is released about a database containing sensitive information about individuals. We demonstrate that, ignoring computational constraints, it is possible to privately agnostically learn any concept class using a sample size approximately logarithmic in the cardinality of the concept class. Therefore, almost anything learnable is learnable privately: specifically, if a concept class is learnable by a (non-private) algorithm with polynomial sample complexity and output size, then it can be learned privately using a polynomial number of samples. We also present a computationally efficient private PAC learner for the class of parity functions. Local (or randomized response) algorithms are a practical class of private algorithms that have received extensive investigation. We provide a precise characterization of local private learning algorithms. We show that a concept class is learnable by a local algorithm if and only if it is learnable in the statistical query (SQ) model. Finally, we present a separation between the power of interactive and noninteractive local learning algorithms.

Connected through hyperedge HE-1 (induced by source paper "locally private hypothesis selection"):
  with members:
    - "private hypothesis selection"
    - "pan private uniformity testing"
    - "sorted top k in rounds"
    - "improved differentially private analysis of variance"
    - "locally private learning without interaction requires separation"
    - "exponential separations in local differential privacy through communication complexity"
    - "the role of interactivity in local differential privacy"
    - "private identity testing for high dimensional distributions"

Connected through hyperedge HE-2 (induced by source paper "pure differentially private summation from anonymous messages"):
  with members:
    - "on the power of multiple anonymous messages"
    - "improved summation from shuffling"
    - "separating local shuffled differential privacy via histograms"
    - "the privacy blanket of the shuffle model"
    - "differentially private summation with multi message shuffling"
    - "private aggregation from fewer anonymous messages"
    - "scalable and differentially private distributed aggregation in the shuffled model"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-3 (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-4 (induced by source paper "locally private distributed reinforcement learning"):
  with members:
    - "microscopic traffic simulation by cooperative multi agent deep reinforcement learning"
    - "collecting and analyzing multidimensional data with local differential privacy"
    - "local differential privacy for deep learning"
    - "negative update intervals in deep multi agent reinforcement learning"
    - "comparing population means under local differential privacy with significance and power"
    - "robust adversarial reinforcement learning"
    - "noisy networks for exploration"
    - "collecting telemetry data privately"

Connected through hyperedge HE-5 (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-6 (induced by source paper "private summation in the multi message shuffle model"):
  with members:
    - "on the power of multiple anonymous messages"
    - "the privacy blanket of the shuffle model"
    - "private aggregation from fewer anonymous messages"
    - "scalable and differentially private distributed aggregation in the shuffled model"
    - "manipulation attacks in local differential privacy"
    - "distributed differential privacy via shuffling"
    - "prochlo strong privacy for analytics in the crowd"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-7 (induced by source paper "the privacy funnel from the viewpoint of local differential privacy"):
  with members:
    - "encode shuffle analyze privacy revisited formalizations and empirical evaluation"
    - "a submodularity based agglomerative clustering algorithm for the privacy funnel"
    - "information theoretic metrics for local differential privacy protocols"
    - "on perfect privacy and maximal correlation"
    - "principal inertia components and applications"
    - "differential privacy as a mutual information constraint"
    - "extremal mechanisms for local differential privacy"

Connected through hyperedge HE-8 (induced by source paper "efficient noise tolerant and private learning via boosting"):
  with members:
    - "a new analysis of differential privacy s generalization guarantees"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "algorithmic stability for adaptive data analysis"
    - "privately solving linear programs"
    - "differential privacy for the analyst via private equilibrium computation"
    - "calibrating data to sensitivity in private data analysis"

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)