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: "sequential decoding for lossless streaming source coding with side information"
  Abstract: The problem of lossless fixed-rate streaming coding of discrete memoryless sources with side information at the decoder is studied. A random time-varying tree-code is used to sequentially bin strings and a Stack Algorithm with a variable bias uses the side information to give a delay-universal coding system for lossless source coding with side information. The scheme is shown to give exponentially decaying probability of error with delay, with exponent equal to Gallager's random coding exponent for sources with side information. The mean of the random variable of computation for the stack decoder is bounded, and conditions on the bias are given to guarantee a finite $\rho^{th}$ moment for $0 \leq \rho \leq 1$. #R##N#Further, the problem is also studied in the case where there is a discrete memoryless channel between encoder and decoder. The same scheme is slightly modified to give a joint-source channel encoder and Stack Algorithm-based sequential decoder using side information. Again, by a suitable choice of bias, the probability of error decays exponentially with delay and the random variable of computation has a finite mean. Simulation results for several examples are given.

Connected through hyperedge HE-1 (induced by source paper "achievable moderate deviations asymptotics for streaming compression of correlated sources"):
  with members:
    - "secure multiplex coding with dependent and non uniform multiple messages"
    - "exact moderate deviation asymptotics in streaming data transmission"
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "streaming data transmission in the moderate deviations and central limit regimes"
    - "asymmetric evaluations of erasure and undetected error probabilities"
    - "the streaming dmt of fading channels"
    - "moderate deviations in channel coding"
    - "linear error correcting codes with anytime reliability"

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)