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: "on maximal correlation mutual information and data privacy"
  Abstract: The rate-privacy function is defined in \cite{Asoodeh} as a tradeoff between privacy and utility in a distributed private data system in which both privacy and utility are measured using mutual information. Here, we use maximal correlation in lieu of mutual information in the privacy constraint. We first obtain some general properties and bounds for maximal correlation and then modify the rate-privacy function to account for the privacy-constrained estimation problem. We find a bound for the utility in this problem when the maximal correlation privacy is set to some threshold $\epsilon>0$ and construct an explicit privacy scheme which achieves this bound.

Connected through hyperedge HE-1 (induced by source paper "local information privacy and its application to privacy preserving data aggregation"):
  with members:
    - "context aware data aggregation with localized information privacy"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "practical locally private heavy hitters"
    - "context aware generative adversarial privacy"
    - "privacy aware mmse estimation"
    - "differential privacy as a mutual information constraint"
    - "concentrated differential privacy"
    - "privacy preserving analysis of correlated data"

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"
    - "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-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"
    - "information extraction under privacy constraints"

Connected through hyperedge HE-4 (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-5 (induced by source paper "correlated embeddings of pairs of dependent random variables"):
  with members:
    - "on the sample complexity of hgr maximal correlation functions"
    - "a survey of multi view representation learning"
    - "an efficient approach to informative feature extraction from multimodal data"
    - "deep generalized canonical correlation analysis"
    - "a generalized power iteration method for solving quadratic problem on the stiefel manifold"
    - "deep variational canonical correlation analysis"
    - "principal inertia components and applications"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-6 (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"
    - "notes on information theoretic 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-7 (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"

Connected through hyperedge HE-8 (induced by source paper "hypothesis testing under mutual information privacy constraints in the high privacy regime"):
  with members:
    - "privacy aware mmse estimation"
    - "hypothesis testing in the high privacy limit"
    - "f divergence inequalities"
    - "euclidean information theory of networks"
    - "extremal mechanisms for local differential privacy"
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "bounds on inference"
    - "the awgn red alert problem"

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)