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: "from the information bottleneck to the privacy funnel"
  Abstract: We focus on the privacy-utility trade-off encountered by users who wish to disclose some information to an analyst, that is correlated with their private data, in the hope of receiving some utility. We rely on a general privacy statistical inference framework, under which data is transformed before it is disclosed, according to a probabilistic privacy mapping. We show that when the log-loss is introduced in this framework in both the privacy metric and the distortion metric, the privacy leakage and the utility constraint can be reduced to the mutual information between private data and disclosed data, and between non-private data and disclosed data respectively. We justify the relevance and generality of the privacy metric under the log-loss by proving that the inference threat under any bounded cost function can be upper-bounded by an explicit function of the mutual information between private data and disclosed data. We then show that the privacy-utility tradeoff under the log-loss can be cast as the non-convex Privacy Funnel optimization, and we leverage its connection to the Information Bottleneck, to provide a greedy algorithm that is locally optimal. We evaluate its performance on the US census dataset.

Connected through hyperedge HE-1 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "machine learning using the variational predictive information bottleneck with a validation set"
    - "on the difference between the information bottleneck and the deep information bottleneck"
    - "extracting robust and accurate features via a robust information bottleneck"
    - "general information bottleneck objectives and their applications to machine learning"
    - "vector gaussian ceo problem under logarithmic loss"
    - "on universal features for high dimensional learning and inference"
    - "estimation efficiency under privacy constraints"

Connected through hyperedge HE-2 (induced by source paper "a practical approach to navigating the tradeoff between privacy and precise utility"):
  with members:
    - "the utility cost of robust privacy guarantees"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "an estimation theoretic view of privacy"
    - "on maximal correlation mutual information and data privacy"
    - "maximal correlation secrecy"
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "privacy against statistical inference"
    - "smart meter privacy a utility privacy framework"

Connected through hyperedge HE-3 (induced by source paper "privacy utility tradeoff in a guessing framework inspired by index coding"):
  with members:
    - "an operational approach to information leakage"
    - "lexicographic products and the power of non linear network coding"
    - "broadcasting with side information"

Connected through hyperedge HE-4 (induced by source paper "privacy aware distributed hypothesis testing"):
  with members:
    - "privacy aware smart metering progress and challenges"
    - "distributed hypothesis testing with privacy constraints"
    - "technical privacy metrics a systematic survey"
    - "distributed hypothesis testing based on unequal error protection codes"
    - "privacy against a hypothesis testing adversary"
    - "on the reliability function of distributed hypothesis testing under optimal detection"
    - "distributed hypothesis testing over discrete memoryless channels"
    - "hypothesis testing under mutual information privacy constraints in the high privacy regime"

Connected through hyperedge HE-5 (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-6 (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"
    - "differential privacy as a mutual information constraint"

Connected through hyperedge HE-7 (induced by source paper "estimation efficiency under privacy constraints"):
  with members:
    - "technical privacy metrics a systematic survey"
    - "generalizing bottleneck problems"
    - "privacy aware guessing efficiency"
    - "hypothesis testing under mutual information privacy constraints in the high privacy regime"
    - "principal inertia components and applications"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "privacy aware mmse estimation"
    - "notes on information theoretic privacy"

Connected through hyperedge HE-8 (induced by source paper "data disclosure under perfect sample privacy"):
  with members:
    - "quantifying high order interdependencies via multivariate extensions of the mutual information"
    - "generative adversarial privacy"
    - "on perfect privacy and maximal correlation"
    - "privacy utility tradeoffs under constrained data release mechanisms"
    - "quantifying synergistic information using intermediate stochastic variables"
    - "understanding interdependency through complex information sharing"
    - "notes on information theoretic 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.IT(Information Theory)