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: "privacy preserving prediction"
  Abstract: Ensuring differential privacy of models learned from sensitive user data is an important goal that has been studied extensively in recent years. It is now known that for some basic learning problems, especially those involving high-dimensional data, producing an accurate private model requires much more data than learning without privacy. At the same time, in many applications it is not necessary to expose the model itself. Instead users may be allowed to query the prediction model on their inputs only through an appropriate interface. Here we formulate the problem of ensuring privacy of individual predictions and investigate the overheads required to achieve it in several standard models of classification and regression. #R##N#We first describe a simple baseline approach based on training several models on disjoint subsets of data and using standard private aggregation techniques to predict. We show that this approach has nearly optimal sample complexity for (realizable) PAC learning of any class of Boolean functions. At the same time, without strong assumptions on the data distribution, the aggregation step introduces a substantial overhead. We demonstrate that this overhead can be avoided for the well-studied class of thresholds on a line and for a number of standard settings of convex regression. The analysis of our algorithm for learning thresholds relies crucially on strong generalization guarantees that we establish for all differentially private prediction algorithms.

Connected through hyperedge HE-1 (induced by source paper "privgen preserving privacy of sequences through data generation"):
  with members:
    - "differentially private generative adversarial networks for time series continuous and discrete open data"
    - "understanding membership inferences on well generalized learning models"
    - "plausible deniability for privacy preserving data synthesis"
    - "k τ e anonymity towards privacy preserving publishing of spatiotemporal trajectory data"
    - "generating multi label discrete patient records using generative adversarial networks"
    - "individual differential privacy a utility preserving formulation of differential privacy guarantees"
    - "privacy risk in machine learning analyzing the connection to overfitting"
    - "privtree a differentially private algorithm for hierarchical decompositions"

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"
    - "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"
    - "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"
    - "understanding membership inferences on well generalized learning models"

Connected through hyperedge HE-3 (induced by source paper "adversarial neural network inversion via auxiliary knowledge alignment"):
  with members:
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "i revnet deep invertible networks"
    - "gazelle a low latency framework for secure neural network inference"
    - "the reversible residual network backpropagation without storing activations"
    - "magnet a two pronged defense against adversarial examples"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "machine learning models that remember too much"
    - "deep models under the gan information leakage from collaborative deep learning"

Connected through hyperedge HE-4 (induced by source paper "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"):
  with members:
    - "hypothesis set stability and generalization"
    - "generalization bounds for uniformly stable algorithms"
    - "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-5 (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"
    - "towards measuring membership privacy"
    - "subgaussian tail bounds via stability arguments"
    - "learning privately from multiparty data"

Connected through hyperedge HE-6 (induced by source paper "private center points and learning of halfspaces"):
  with members:
    - "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"
    - "learning privately with labeled and unlabeled examples"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-7 (induced by source paper "differentially private learning of geometric concepts"):
  with members:
    - "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"
    - "learning privately with labeled and unlabeled examples"
    - "differentially private combinatorial optimization"

Connected through hyperedge HE-8 (induced by source paper "developing non stochastic privacy preserving policies using agglomerative clustering"):
  with members:
    - "robustness of maximal alpha leakage to side information"
    - "a submodularity based agglomerative clustering algorithm for the privacy funnel"
    - "tunable measures for information leakage and applications to privacy utility tradeoffs"
    - "uncertain wiretap channels and secure estimation"
    - "notes on information theoretic privacy"
    - "from the information bottleneck to the privacy funnel"
    - "a nonstochastic information theory for communication and state estimation"
    - "privacy against statistical inference"

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)