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: "the utility cost of robust privacy guarantees"
  Abstract: Consider a data publishing setting for a data set with public and private features. The objective of the publisher is to maximize the amount of information about the public features in a revealed data set, while keeping the information leaked about the private features bounded. The goal of this paper is to analyze the performance of privacy mechanisms that are constructed to match the distribution learned from the data set. Two distinct scenarios are considered: (i) mechanisms are designed to provide a privacy guarantee for the learned distribution; and (ii) mechanisms are designed to provide a privacy guarantee for every distribution in a given neighborhood of the learned distribution. For the first scenario, given any privacy mechanism, upper bounds on the difference between the privacy-utility guarantees for the learned and true distributions are presented. In the second scenario, upper bounds on the reduction in utility incurred by providing a uniform privacy guarantee are developed.

Connected through hyperedge HE-1 (induced by source paper "a practical approach to navigating the tradeoff between privacy and precise utility"):
  with members:
    - "hypothesis testing under maximal leakage privacy constraints"
    - "an estimation theoretic view of privacy"
    - "on maximal correlation mutual information and data privacy"
    - "maximal correlation secrecy"
    - "from the information bottleneck to the privacy funnel"
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "privacy against statistical inference"
    - "smart meter privacy a utility privacy framework"

Connected through hyperedge HE-2 (induced by source paper "on the robustness of information theoretic privacy measures and mechanisms"):
  with members:
    - "data disclosure under perfect sample privacy"
    - "privacy against brute force inference attacks"
    - "estimation efficiency under privacy constraints"
    - "data privacy for a rho recoverable function"
    - "an operational approach to information leakage"
    - "privacy under hard distortion constraints"
    - "a tunable measure for information leakage"
    - "technical privacy metrics a systematic survey"

Connected through hyperedge HE-3 (induced by source paper "privacy with estimation guarantees"):
  with members:
    - "generalizing bottleneck problems"
    - "matching anonymized and obfuscated time series to users profiles"
    - "privacy preserving adversarial networks"
    - "renyi differential privacy"
    - "principal inertia components and applications"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "an estimation theoretic view of privacy"
    - "privacy aware mmse estimation"

Connected through hyperedge HE-4 (induced by source paper "optimal utility privacy trade off with the total variation distance as the privacy measure"):
  with members:
    - "estimation efficiency under privacy constraints"
    - "a tunable measure for information leakage"
    - "vgan based image representation learning for privacy preserving facial expression recognition"
    - "privacy preserving adversarial networks"
    - "privacy aware guessing efficiency"
    - "privacy utility tradeoffs under constrained data release mechanisms"
    - "an estimation theoretic view of privacy"
    - "privacy aware mmse estimation"

Connected through hyperedge HE-5 (induced by source paper "privacy under hard distortion constraints"):
  with members:
    - "optimal utility privacy trade off with the total variation distance as the privacy measure"
    - "a tunable measure for information leakage"
    - "hypothesis testing under mutual information privacy constraints in the high privacy regime"
    - "privacy aware mmse estimation"
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "privacy against statistical inference"

Connected through hyperedge HE-6 (induced by source paper "an estimation theoretic view of privacy"):
  with members:
    - "generalizing bottleneck problems"
    - "matching anonymized and obfuscated time series to users profiles"
    - "privacy preserving adversarial networks"
    - "context aware generative adversarial privacy"
    - "principal inertia components and applications"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "privacy aware mmse estimation"
    - "hypothesis testing in the high privacy limit"

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)