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: "indistinguishability and energy sensitivity of asymptotically gaussian compressed encryption"
  Abstract: The principle of compressed sensing (CS) can be applied in a cryptosystem by providing the notion of security. In information-theoretic sense, it is known that a CS-based cryptosystem can be perfectly secure if it employs a random Gaussian sensing matrix updated at each encryption and its plaintext has constant energy. In this paper, we propose a new CS-based cryptosystem that employs a secret bipolar keystream and a public unitary matrix, which can be suitable for practical implementation by generating and renewing the keystream in a fast and efficient manner. We demonstrate that the sensing matrix is asymptotically Gaussian for a sufficiently large plaintext length, which guarantees a reliable CS decryption for a legitimate recipient. By means of probability metrics, we also show that the new CS-based cryptosystem can have the indistinguishability against an adversary, as long as the keystream is updated at each encryption and each plaintext has constant energy. Finally, we investigate how much the security of the new CS-based cryptosystem is sensitive to energy variation of plaintexts.

Connected through hyperedge HE-1 (induced by source paper "secure and efficient compressed sensing based encryption with sparse matrices"):
  with members:
    - "a survey on wireless security technical challenges recent advances and future trends"
    - "bi level protected compressive sampling"
    - "on known plaintext attacks to a compressed sensing based encryption a quantitative analysis"
    - "low complexity multiclass encryption by compressed sensing"
    - "mergeshuffle a very fast parallel random permutation algorithm"
    - "permutation meets parallel compressed sensing how to relax restricted isometry property for 2d sparse signals"
    - "compressive mechanism utilizing sparse representation in differential privacy"
    - "a compressed sensing wire tap channel"

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)