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: "differential privacy as a mutual information constraint"
  Abstract: Differential privacy is a precise mathematical constraint meant to ensure privacy of individual pieces of information in a database even while queries are being answered about the aggregate. Intuitively, one must come to terms with what differential privacy does and does not guarantee. For example, the definition prevents a strong adversary who knows all but one entry in the database from further inferring about the last one. This strong adversary assumption can be overlooked, resulting in misinterpretation of the privacy guarantee of differential privacy. Herein we give an equivalent definition of privacy using mutual information that makes plain some of the subtleties of differential privacy. The mutual-information differential privacy is in fact sandwiched between e-differential privacy and (e,δ)-differential privacy in terms of its strength. In contrast to previous works using unconditional mutual information, differential privacy is fundamentally related to conditional mutual information, accompanied by a maximization over the database distribution. The conceptual advantage of using mutual information, aside from yielding a simpler and more intuitive definition of differential privacy, is that its properties are well understood. Several properties of differential privacy are easily verified for the mutual information alternative, such as composition theorems.

Connected through hyperedge HE-1 (induced by source paper "local information privacy and its application to privacy preserving data aggregation"):
  with members:
    - "context aware data aggregation with localized information privacy"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "practical locally private heavy hitters"
    - "context aware generative adversarial privacy"
    - "privacy aware mmse estimation"
    - "concentrated differential privacy"
    - "privacy preserving analysis of correlated data"
    - "on maximal correlation mutual information and data privacy"

Connected through hyperedge HE-2 (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"
    - "extremal mechanisms for local differential privacy"
    - "what can we learn privately"

Connected through hyperedge HE-3 (induced by source paper "obfuscation via information density estimation"):
  with members:
    - "privacy amplification by mixing and diffusion mechanisms"
    - "estimation efficiency under privacy constraints"
    - "distributed generation of privacy preserving data with user customization"
    - "on the robustness of information theoretic privacy measures and mechanisms"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "generative adversarial privacy"
    - "an operational approach to information leakage"
    - "a tunable measure for information leakage"

Connected through hyperedge HE-4 (induced by source paper "information theoretic metrics for local differential privacy protocols"):
  with members:
    - "consistent and accurate frequency oracles under local differential privacy"
    - "towards instance optimal private query release"
    - "distributed and secure ml with self tallying multi party aggregation"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "renyi differential privacy"
    - "achieving secure and differentially private computations in multiparty settings"
    - "collecting telemetry data privately"
    - "mutual information optimally local private discrete distribution estimation"

Connected through hyperedge HE-5 (induced by source paper "local distribution obfuscation via probability coupling"):
  with members:
    - "local obfuscation mechanisms for hiding probability distributions"
    - "sok differential privacies"
    - "profile based privacy for locally private computations"
    - "utility optimized local differential privacy mechanisms for distribution estimation"
    - "renyi differential privacy"
    - "on the compositionality of quantitative information flow"
    - "concentrated differential privacy"
    - "rappor randomized aggregatable privacy preserving ordinal response"

Connected through hyperedge HE-6 (induced by source paper "generating optimal privacy protection mechanisms via machine learning"):
  with members:
    - "privacy preserving adversarial networks"
    - "back to the drawing board revisiting the design of optimal location privacy preserving mechanisms"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "privacy games optimal user centric data obfuscation"
    - "optimal geo indistinguishable mechanisms for location privacy"
    - "geo indistinguishability differential privacy for location based systems"
    - "compositional closure for bayes risk in probabilistic noninterference"
    - "on an improvement over renyi s equivocation bound"

Connected through hyperedge HE-7 (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"
    - "inferential privacy guarantees for differentially private mechanisms"
    - "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-8 (induced by source paper "utility optimized local differential privacy mechanisms for distribution estimation"):
  with members:
    - "blender enabling local search with a hybrid differential privacy model"
    - "one sided differential privacy"
    - "renyi differential privacy"
    - "back to the drawing board revisiting the design of optimal location privacy preserving mechanisms"
    - "collecting telemetry data privately"
    - "pufferfish privacy mechanisms for correlated data"
    - "privacy games optimal user centric data obfuscation"
    - "heterogeneous differential privacy"

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.IT(Information Theory)