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: "joint source channel secrecy using hybrid coding"
  Abstract: The secrecy performance of a source-channel model is studied in the context of lossy source compression over a noisy broadcast channel. The source is causally revealed to the eavesdropper during decoding. The fidelity of the transmission to the legitimate receiver and the secrecy performance at the eavesdropper are both measured by a distortion metric. Two achievability schemes using the technique of hybrid coding are analyzed and compared with an operationally separate source-channel coding scheme. A numerical example is provided and the comparison results show that the hybrid coding schemes outperform the operationally separate scheme.

Connected through hyperedge HE-1 (induced by source paper "the likelihood encoder for lossy compression"):
  with members:
    - "a rate distortion based secrecy system with side information at the decoders"
    - "a generalized typicality for abstract alphabets"
    - "the likelihood encoder for lossy source compression"
    - "a technique for deriving one shot achievability results in network information theory"
    - "non asymptotic output statistics of random binning and its applications"
    - "a connection between good rate distortion codes and backward dmcs"
    - "the likelihood encoder for source coding"
    - "distributed channel synthesis"

Connected through hyperedge HE-2 (induced by source paper "source channel secrecy for shannon cipher system"):
  with members:
    - "joint source channel secrecy using uncoded schemes towards secure source broadcast"
    - "the henchman problem measuring secrecy by the minimum distortion in a list"
    - "secure transmission of sources over noisy channels with side information at the receivers"
    - "information spectrum approach to strong converse theorems for degraded wiretap channels"
    - "information spectrum approach to the source channel separation theorem"
    - "lossy joint source channel coding in the finite blocklength regime"
    - "transmitting an analog gaussian source over a gaussian wiretap channel under snr mismatch"

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)