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: "information extraction under privacy constraints"
  Abstract: A privacy-constrained information extraction problem is considered where for a pair of correlated discrete random variables $(X,Y)$ governed by a given joint distribution, an agent observes $Y$ and wants to convey to a potentially public user as much information about $Y$ as possible without compromising the amount of information revealed about $X$. To this end, the so-called {\em rate-privacy function} is introduced to quantify the maximal amount of information (measured in terms of mutual information) that can be extracted from $Y$ under a privacy constraint between $X$ and the extracted information, where privacy is measured using either mutual information or maximal correlation. Properties of the rate-privacy function are analyzed and information-theoretic and estimation-theoretic interpretations of it are presented for both the mutual information and maximal correlation privacy measures. It is also shown that the rate-privacy function admits a closed-form expression for a large family of joint distributions of $(X,Y)$. Finally, the rate-privacy function under the mutual information privacy measure is considered for the case where $(X,Y)$ has a joint probability density function by studying the problem where the extracted information is a uniform quantization of $Y$ corrupted by additive Gaussian noise. The asymptotic behavior of the rate-privacy function is studied as the quantization resolution grows without bound and it is observed that not all of the properties of the rate-privacy function carry over from the discrete to the continuous case.

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 "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-3 (induced by source paper "maximal information leakage based privacy preserving data disclosure mechanisms"):
  with members:
    - "privacy preserving adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "minimax filter learning to preserve privacy from inference attacks"
    - "learning privately from multiparty data"
    - "improved techniques for training gans"
    - "vime variational information maximizing exploration"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "on maximal correlation mutual information and data privacy"

Connected through hyperedge HE-4 (induced by source paper "broadcasting information subject to state masking over a mimo state dependent gaussian channel"):
  with members:
    - "broadcasting information subject to state masking"
    - "information masking and amplification the source coding setting"
    - "state amplification subject to masking constraints"
    - "capacity bounds for multiuser channels with non causal channel state information at the transmitters"
    - "an extremal inequality motivated by multiterminal information theoretic problems"

Connected through hyperedge HE-5 (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"
    - "the utility cost of robust privacy guarantees"
    - "privacy under hard distortion constraints"
    - "a tunable measure for information leakage"

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

Connected through hyperedge HE-8 (induced by source paper "decentralized detection with robust information privacy protection"):
  with members:
    - "solving linear programs in the current matrix multiplication time"
    - "robust privacy utility tradeoffs under differential privacy and hamming distortion"
    - "multilayer nonlinear processing for information privacy in sensor networks"
    - "hypothesis testing under mutual information privacy constraints in the high privacy regime"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "robust decentralized detection and social learning in tandem networks"
    - "whose opinion to follow in multihypothesis social learning a large deviations perspective"
    - "big data privacy in the internet of things era"

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)