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: "notes on information theoretic privacy"
  Abstract: We investigate the tradeoff between privacy and utility in a situation where both privacy and utility are measured in terms of mutual information. For the binary case, we fully characterize this tradeoff in case of perfect privacy and also give an upper-bound for the case where some privacy leakage is allowed. We then introduce a new quantity which quantifies the amount of private information contained in the observable data and then connect it to the optimal tradeoff between privacy and utility.

Connected through hyperedge HE-1 (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"
    - "information extraction under privacy constraints"

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"
    - "privacy utility tradeoffs under constrained data release mechanisms"
    - "quantifying synergistic information using intermediate stochastic variables"
    - "understanding interdependency through complex information sharing"
    - "from the information bottleneck to the privacy funnel"
    - "privacy against statistical inference"

Connected through hyperedge HE-3 (induced by source paper "developing non stochastic privacy preserving policies using agglomerative clustering"):
  with members:
    - "robustness of maximal alpha leakage to side information"
    - "a submodularity based agglomerative clustering algorithm for the privacy funnel"
    - "tunable measures for information leakage and applications to privacy utility tradeoffs"
    - "privacy preserving prediction"
    - "uncertain wiretap channels and secure estimation"
    - "from the information bottleneck to the privacy funnel"
    - "a nonstochastic information theory for communication and state estimation"
    - "privacy against statistical inference"

Connected through hyperedge HE-4 (induced by source paper "differential privacy techniques for cyber physical systems a survey"):
  with members:
    - "quantifying differential privacy in continuous data release under temporal correlations"
    - "sok differential privacies"
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "achieving differential privacy against non intrusive load monitoring in smart grid a fog computing approach"
    - "toward practical privacy preserving analytics for iot and cloud based healthcare systems"
    - "privacy preserving distributed deep learning for clinical data"
    - "re dpoctor real time health data releasing with w day differential privacy"

Connected through hyperedge HE-5 (induced by source paper "privacy with estimation guarantees"):
  with members:
    - "the utility cost of robust privacy guarantees"
    - "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"

Connected through hyperedge HE-6 (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"
    - "privacy utility tradeoffs under constrained data release mechanisms"
    - "an estimation theoretic view of privacy"

Connected through hyperedge HE-7 (induced by source paper "context aware data aggregation with localized information privacy"):
  with members:
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "practical locally private heavy hitters"
    - "quantifying differential privacy under temporal correlations"
    - "privacy aware mmse estimation"
    - "on maximal correlation mutual information and data privacy"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "on the relation between identifiability differential privacy and mutual information privacy"
    - "extremal mechanisms for local differential privacy"

Connected through hyperedge HE-8 (induced by source paper "context aware generative adversarial privacy"):
  with members:
    - "estimation efficiency under privacy constraints"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "information theoretic privacy with general distortion constraints"
    - "ensemble estimation of mutual information"
    - "deeprotect enabling inference based access control on mobile sensing applications"
    - "information potential auto encoders"
    - "privacy aware mmse estimation"
    - "minimax filter learning to preserve privacy from inference attacks"

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)