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: "inferential privacy guarantees for differentially private mechanisms"
  Abstract: The correlations and network structure amongst individuals in datasets today---whether explicitly articulated, or deduced from biological or behavioral connections---pose new issues around privacy guarantees, because of inferences that can be made about one individual from another's data. This motivates quantifying privacy in networked contexts in terms of "inferential privacy"---which measures the change in beliefs about an individual's data from the result of a computation---as originally proposed by Dalenius in the 1970's. Inferential privacy is implied by differential privacy when data are independent, but can be much worse when data are correlated; indeed, simple examples, as well as a general impossibility theorem of Dwork and Naor, preclude the possibility of achieving non-trivial inferential privacy when the adversary can have arbitrary auxiliary information. In this paper, we ask how differential privacy guarantees translate to guarantees on inferential privacy in networked contexts: specifically, under what limitations on the adversary's information about correlations, modeled as a prior distribution over datasets, can we deduce an inferential guarantee from a differential one? #R##N#We prove two main results. The first result pertains to distributions that satisfy a natural positive-affiliation condition, and gives an upper bound on the inferential privacy guarantee for any differentially private mechanism. This upper bound is matched by a simple mechanism that adds Laplace noise to the sum of the data. The second result pertains to distributions that have weak correlations, defined in terms of a suitable "influence matrix". The result provides an upper bound for inferential privacy in terms of the differential privacy parameter and the spectral norm of this matrix.

Connected through hyperedge HE-1 (induced by source paper "differentially private model publishing for deep learning"):
  with members:
    - "machine learning models that remember too much"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "understanding deep learning requires rethinking generalization"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "stealing machine learning models via prediction apis"
    - "concentrated differential privacy"
    - "membership inference attacks against machine learning models"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-2 (induced by source paper "a necessary and sufficient stability notion for adaptive generalization"):
  with members:
    - "natural analysts in adaptive data analysis"
    - "an operational approach to information leakage"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "calibrating noise to variance in adaptive data analysis"
    - "learners that use little information"
    - "learners that leak little information"
    - "typicality based stability and privacy"
    - "adaptive learning with robust generalization guarantees"

Connected through hyperedge HE-3 (induced by source paper "constructing privacy channels from information channels"):
  with members:
    - "on the information privacy model the group and composition privacy"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "max information differential privacy and post selection hypothesis testing"
    - "generalization in adaptive data analysis and holdout reuse"
    - "principled evaluation of differentially private algorithms using dpbench"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-4 (induced by source paper "on privacy of socially contagious attributes"):
  with members:
    - "ties that bind characterizing classes by attributes and social ties"
    - "pufferfish privacy mechanisms for correlated data"
    - "multiple infection sources identification with provable guarantees"
    - "identifying infection sources and regions in large networks"
    - "maximizing social influence in nearly optimal time"
    - "inferring networks of diffusion and influence"

Connected through hyperedge HE-5 (induced by source paper "on the information privacy model the group and composition privacy"):
  with members:
    - "information theory of data privacy"
    - "privtree a differentially private algorithm for hierarchical decompositions"
    - "calibrating data to sensitivity in private data analysis"
    - "composition attacks and auxiliary information in data privacy"

Connected through hyperedge HE-6 (induced by source paper "compressive privacy for a linear dynamical system"):
  with members:
    - "plausible deniability for privacy preserving data synthesis"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "information theoretic attacks in the smart grid"
    - "privacy aware mmse estimation"
    - "differential privacy as a mutual information constraint"
    - "distributed local linear parameter estimation using gaussian spawn"
    - "robust decentralized detection and social learning in tandem networks"
    - "whose opinion to follow in multihypothesis social learning a large deviations perspective"

Connected through hyperedge HE-7 (induced by source paper "composition properties of inferential privacy for time series data"):
  with members:
    - "pufferfish privacy mechanisms for correlated data"
    - "blowfish privacy tuning privacy utility trade offs using policies"
    - "the composition theorem for differential privacy"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-8 (induced by source paper "pufferfish privacy mechanisms for correlated data"):
  with members:
    - "protecting locations with differential privacy under temporal correlations"
    - "the large margin mechanism for differentially private maximization"
    - "differentially private empirical risk minimization efficient algorithms and tight error bounds"
    - "blowfish privacy tuning privacy utility trade offs using policies"
    - "differentially private algorithms for empirical machine learning"
    - "calibrating data to sensitivity in private data analysis"
    - "an adaptive mechanism for accurate query answering under differential privacy"
    - "beyond worst case analysis in private singular vector computation"

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)