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: "privacy utility tradeoffs under constrained data release mechanisms"
  Abstract: Privacy-preserving data release mechanisms aim to simultaneously minimize information-leakage with respect to sensitive data and distortion with respect to useful data. Dependencies between sensitive and useful data results in a privacy-utility tradeoff that has strong connections to generalized rate-distortion problems. In this work, we study how the optimal privacy-utility tradeoff region is affected by constraints on the data that is directly available as input to the release mechanism. In particular, we consider the availability of only sensitive data, only useful data, and both (full data). We show that a general hierarchy holds: the tradeoff region given only the sensitive data is no larger than the region given only the useful data, which in turn is clearly no larger than the region given both sensitive and useful data. In addition, we determine conditions under which the tradeoff region given only the useful data coincides with that given full data. These are based on the common information between the sensitive and useful data. We establish these results for general families of privacy and utility measures that satisfy certain natural properties required of any reasonable measure of privacy or utility. We also uncover a new, subtler aspect of the data processing inequality for general non-symmetric privacy measures and discuss its operational relevance and implications. Finally, we derive exact closed-analytic-form expressions for the privacy-utility tradeoffs for symmetrically dependent sensitive and useful data under mutual information and Hamming distortion as the respective privacy and utility measures.

Connected through hyperedge HE-1 (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-2 (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"
    - "quantifying synergistic information using intermediate stochastic variables"
    - "understanding interdependency through complex information sharing"
    - "notes on information theoretic privacy"
    - "from the information bottleneck to the privacy funnel"
    - "privacy against statistical inference"

Connected through hyperedge HE-3 (induced by source paper "privacy against brute force inference attacks"):
  with members:
    - "estimation efficiency under privacy constraints"
    - "a tunable measure for information leakage"
    - "multi user guesswork and brute force security"
    - "from the information bottleneck to the privacy funnel"
    - "privacy against statistical inference"
    - "guessing revisited a large deviations approach"

Connected through hyperedge HE-4 (induced by source paper "distributed hypothesis testing with a privacy constraint"):
  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"
    - "hypothesis testing under mutual information privacy constraints in the high privacy regime"
    - "on binary distributed hypothesis testing"

Connected through hyperedge HE-5 (induced by source paper "tunable measures for information leakage and applications to privacy utility tradeoffs"):
  with members:
    - "robustness of maximal alpha leakage to side information"
    - "data privacy for a rho recoverable function"
    - "an operational approach to information leakage"
    - "optimal utility privacy trade off with the total variation distance as the privacy measure"
    - "robust privacy utility tradeoffs under differential privacy and hamming distortion"
    - "privacy aware guessing efficiency"
    - "renyi differential privacy"
    - "hypothesis testing under mutual information privacy constraints in the high privacy regime"

Connected through hyperedge HE-6 (induced by source paper "an operational approach to information leakage"):
  with members:
    - "estimation efficiency under privacy constraints"
    - "technical privacy metrics a systematic survey"
    - "measuring secrecy by the probability of a successful guess"
    - "principal inertia components and applications"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "a large deviations approach to secure lossy compression"
    - "mitigating timing side channel in shared schedulers"
    - "hiding the rumor source"

Connected through hyperedge HE-7 (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"
    - "the utility cost of robust privacy guarantees"
    - "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"
    - "an estimation theoretic view of privacy"
    - "privacy aware mmse estimation"

Connected through hyperedge HE-8 (induced by source paper "privacy preserving adversarial networks"):
  with members:
    - "generative adversarial privacy"
    - "semi adversarial networks convolutional autoencoders for imparting privacy to face images"
    - "context aware generative adversarial privacy"
    - "minimax filter learning to preserve privacy from inference attacks"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "censoring representations with an adversary"
    - "adam a method for stochastic optimization"

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)