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: "secure lossy transmission of vector gaussian sources"
  Abstract: We study the secure lossy transmission of a vector Gaussian source to a legitimate user in the presence of an eavesdropper, where both the legitimate user and the eavesdropper have vector Gaussian side information. The aim of the transmitter is to describe the source to the legitimate user in a way that the legitimate user can reconstruct the source within a certain distortion level while the eavesdropper is kept ignorant of the source as much as possible as measured by the equivocation. We obtain an outer bound for the rate, equivocation and distortion region of this secure lossy transmission problem. This outer bound is tight when the transmission rate constraint is removed. In other words, we obtain the maximum equivocation at the eavesdropper when the legitimate user needs to reconstruct the source within a fixed distortion level while there is no constraint on the transmission rate. This characterization of the maximum equivocation involves two auxiliary random variables. We show that a non-trivial selection for both random variables may be necessary in general. The necessity of two auxiliary random variables also implies that, in general, Wyner-Ziv coding is suboptimal in the presence of an eavesdropper. In addition, we show that, even when there is no rate constraint on the legitimate link, uncoded transmission (deterministic or stochastic) is suboptimal; the presence of an eavesdropper necessitates the use of a coded scheme to attain the maximum equivocation.

Connected through hyperedge HE-1 (induced by source paper "vector gaussian successive refinement with degraded side information"):
  with members:
    - "the sum rate of vector gaussian multiple description coding with tree structured covariance distortion constraints"
    - "vector gaussian rate distortion with variable side information"
    - "broadcasting correlated vector gaussians"
    - "new results on multiple input multiple output broadcast channels with confidential messages"
    - "private broadcasting over independent parallel channels"
    - "information theoretic proofs of entropy power inequalities"
    - "capacity equivocation region of the gaussian mimo wiretap channel"
    - "capacity region of gaussian mimo broadcast channels with common and confidential messages"

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)