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: "a learning theory approach to non interactive database privacy"
  Abstract: In this paper we demonstrate that, ignoring computational constraints, it is possible to privately release synthetic databases that are useful for large classes of queries -- much larger in size than the database itself. Specifically, we give a mechanism that privately releases synthetic data for a class of queries over a discrete domain with error that grows as a function of the size of the smallest net approximately representing the answers to that class of queries. We show that this in particular implies a mechanism for counting queries that gives error guarantees that grow only with the VC-dimension of the class of queries, which itself grows only logarithmically with the size of the query class. #R##N#We also show that it is not possible to privately release even simple classes of queries (such as intervals and their generalizations) over continuous domains. Despite this, we give a privacy-preserving polynomial time algorithm that releases information useful for all halfspace queries, given a slight relaxation of the utility guarantee. This algorithm does not release synthetic data, but instead another data structure capable of representing an answer for each query. We also give an efficient algorithm for releasing synthetic data for the class of interval queries and axis-aligned rectangles of constant dimension. #R##N#Finally, inspired by learning theory, we introduce a new notion of data privacy, which we call distributional privacy, and show that it is strictly stronger than the prevailing privacy notion, differential privacy.

Connected through hyperedge HE-1 (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-2 (induced by source paper "on the power of multiple anonymous messages"):
  with members:
    - "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"
    - "practical and robust privacy amplification with multi party differential privacy"
    - "communication complexity in locally private distribution estimation and heavy hitters"
    - "private aggregation from fewer anonymous messages"
    - "scalable and differentially private distributed aggregation in the shuffled model"

Connected through hyperedge HE-3 (induced by source paper "differentially private mixed type data generation for unsupervised learning"):
  with members:
    - "dp cgan differentially private synthetic data and label generation"
    - "differentially private learning with adaptive clipping"
    - "differentially private model publishing for deep learning"
    - "differentially private generative adversarial networks for time series continuous and discrete open data"
    - "private selection from private candidates"
    - "differentially private generative adversarial network"
    - "differentially private data generative models"
    - "generating artificial data for private deep learning"

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"
    - "learning privately with labeled and unlabeled examples"

Connected through hyperedge HE-5 (induced by source paper "efficient private algorithms for learning halfspaces"):
  with members:
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"
    - "privacy via the johnson lindenstrauss transform"
    - "fingerprinting codes and the price of approximate differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "differentially private data analysis of social networks via restricted sensitivity"
    - "differentially private empirical risk minimization"
    - "what can we learn privately"

Connected through hyperedge HE-6 (induced by source paper "data masking with privacy guarantees"):
  with members:
    - "understanding the sparse vector technique for differential privacy"
    - "utilizing noise addition for data privacy an overview"

Connected through hyperedge HE-7 (induced by source paper "individual sensitivity preprocessing for data privacy"):
  with members:
    - "blender enabling local search with a hybrid differential privacy model"
    - "tight lower bounds for locally differentially private selection"
    - "the possibilities and limitations of private prediction markets"
    - "adaptive learning with robust generalization guarantees"
    - "efficient lipschitz extensions for high dimensional graph statistics and node private degree distributions"
    - "heterogeneous differential privacy"
    - "truthful linear regression"
    - "buying private data without verification"

Connected through hyperedge HE-8 (induced by source paper "data privacy for a rho recoverable function"):
  with members:
    - "estimation efficiency under privacy constraints"
    - "context aware generative adversarial privacy"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"
    - "local private efficient protocols for succinct histograms"
    - "renyi divergence and kullback leibler divergence"
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "the composition theorem for differential privacy"
    - "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.DS(Data Structures and Algorithms)