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: "a large deviations approach to secure lossy compression"
  Abstract: A Shannon cipher system for memoryless sources is considered, in which distortion is allowed at the legitimate decoder. The source is compressed using a rate distortion code secured by a shared key, which satisfies a constraint on the compression rate, as well as a constraint on the exponential rate of the excess-distortion probability at the legitimate decoder. Secrecy is measured by the exponential rate of the exiguous-distortion probability at the eavesdropper, rather than by the traditional measure of equivocation. The perfect secrecy exponent is defined as the maximal exiguous-distortion exponent achievable when the key rate is unlimited. Under limited key rate, it is proved that the maximal achievable exiguous-distortion exponent is equal to the minimum between the average key rate and the perfect secrecy exponent, for a fairly general class of variable key rate codes.

Connected through hyperedge HE-1 (induced by source paper "importance of small probability events in big data information measures applications and challenges"):
  with members:
    - "local deep feature alignment for unsupervised dimension reduction"
    - "matching users preference under target revenue constraints in data recommendation systems"
    - "differential message importance measure a new approach to the required sampling number in big data structure characterization"
    - "decorrelation of neutral vector variables theory and applications"
    - "group cooperation with optimal resource allocation in wireless powered communication networks"
    - "amplifying inter message distance on information divergence measures in big data"
    - "focusing on a probability element parameter selection of message importance measure in big data"
    - "linear complexity exponentially consistent tests for universal outlying sequence detection"

Connected through hyperedge HE-2 (induced by source paper "information measure similarity theory message importance measure via shannon entropy"):
  with members:
    - "differential message importance measure a new approach to the required sampling number in big data structure characterization"
    - "focusing on a probability element parameter selection of message importance measure in big data"
    - "detecting unexpected obstacles for self driving cars fusing deep learning and geometric modeling"
    - "message importance measure and its application to minority subset detection in big data"

Connected through hyperedge HE-3 (induced by source paper "an operational approach to information leakage"):
  with members:
    - "estimation efficiency under privacy constraints"
    - "technical privacy metrics a systematic survey"
    - "measuring secrecy by the probability of a successful guess"
    - "principal inertia components and applications"
    - "privacy utility tradeoffs under constrained data release mechanisms"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "mitigating timing side channel in shared schedulers"
    - "hiding the rumor source"

Connected through hyperedge HE-4 (induced by source paper "a correlation measure based on vector valued l_p norms"):
  with members:
    - "exact exponent for soft covering"
    - "exact soft covering exponent"
    - "generalizing bottleneck problems"
    - "measuring secrecy by the probability of a successful guess"
    - "r enyi resolvability and its applications to the wiretap channel"
    - "exact random coding secrecy exponents for the wiretap channel"
    - "f divergence inequalities"
    - "semantic security capacity for wiretap channels of type ii"

Connected through hyperedge HE-5 (induced by source paper "on the reliability function of distributed hypothesis testing under optimal detection"):
  with members:
    - "on hypothesis testing against independence with multiple decision centers"
    - "on binary distributed hypothesis testing"
    - "distributed hypothesis testing over noisy channels"
    - "hypothesis testing over the two hop relay network"
    - "distributed binary detection with lossy data compression"
    - "neyman pearson test for zero rate multiterminal hypothesis testing"
    - "collaborative distributed hypothesis testing"
    - "on the reliability function of variable rate slepian wolf coding"

Connected through hyperedge HE-6 (induced by source paper "measuring secrecy by the probability of a successful guess"):
  with members:
    - "mitigating timing side channel in shared schedulers"
    - "the henchman problem measuring secrecy by the minimum distortion in a list"
    - "rate distortion theory for secrecy systems"
    - "improved rate equivocation regions for secure cooperative communication"
    - "lossless compression with security constraints"
    - "anonymous networking amidst eavesdroppers"
    - "shannon s secrecy system with informed receivers and its application to systematic coding for wiretapped channels"

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)