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 perfect privacy and maximal correlation"
  Abstract: The problem of private data disclosure is studied from an information theoretic perspective. Considering a pair of correlated random variables $(X,Y)$, where $Y$ denotes the observed data while $X$ denotes the private latent variables, the following problem is addressed: What is the maximum information that can be revealed about $Y$, while disclosing no information about $X$? Assuming that a Markov kernel maps $Y$ to the revealed information $U$, it is shown that the maximum mutual information between $Y$ and $U$, i.e., $I(Y;U)$, can be obtained as the solution of a standard linear program, when $X$ and $U$ are required to be independent, called \textit{perfect privacy}. This solution is shown to be greater than or equal to the \textit{non-private information about $X$ carried by $Y$.} Maximal information disclosure under perfect privacy is is shown to be the solution of a linear program also when the utility is measured by the reduction in the mean square error, $\mathbb{E}[(Y-U)^2]$, or the probability of error, $\mbox{Pr}\{Y\neq U\}$. For jointly Gaussian $(X,Y)$, it is shown that perfect privacy is not possible if the kernel is applied to only $Y$; whereas perfect privacy can be achieved if the mapping is from both $X$ and $Y$; that is, if the private latent variables can also be observed at the encoder. Next, measuring the utility and privacy by $I(Y;U)$ and $I(X;U)$, respectively, the slope of the optimal utility-privacy trade-off curve is studied when $I(X;U)=0$. Finally, through a similar but independent analysis, an alternative characterization of the maximal correlation between two random variables is provided.

Connected through hyperedge HE-1 (induced by source paper "the privacy funnel from the viewpoint of local differential privacy"):
  with members:
    - "encode shuffle analyze privacy revisited formalizations and empirical evaluation"
    - "a submodularity based agglomerative clustering algorithm for the privacy funnel"
    - "information theoretic metrics for local differential privacy protocols"
    - "principal inertia components and applications"
    - "differential privacy as a mutual information constraint"
    - "extremal mechanisms for local differential privacy"
    - "what can we learn privately"

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"
    - "privacy utility tradeoffs under constrained data release mechanisms"
    - "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"

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)