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: "on error exponents and moderate deviations for lossless streaming compression of correlated sources"
  Abstract: We derive upper and lower bounds for the error exponents of lossless streaming compression of two correlated sources under the blockwise and symbolwise settings. We consider the linear scaling regime in which the delay is a scalar multiple of the number of symbol pairs of interest. We show that for rate pairs satisfying certain constraints, the upper and lower bounds for the error exponent of blockwise codes coincide. For symbolwise codes, the bounds coincide for rate pairs satisfying the aforementioned constraints and a certain condition on the symbol pairs we wish to decode---namely, that their indices are asymptotically comparable to the blocklength. We also derive moderate deviations constants for blockwise and symbolwise codes, leveraging the error exponent results, and using appropriate Taylor series expansions. In particular, for blockwise codes, we derive an information spectrum-type strong converse, giving the complete characterization of the moderate deviations constants. For symbolwise codes, under an additional requirement on the backoff from the first-order fundamental limit, we can show that the moderate deviations constants are the same as the blockwise setting.

Connected through hyperedge HE-1 (induced by source paper "streaming data transmission in the moderate deviations and central limit regimes"):
  with members:
    - "exact moderate deviation asymptotics in streaming data transmission"
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "convolutional codes with maximum column sum rank for network streaming"
    - "asymmetric evaluations of erasure and undetected error probabilities"
    - "unequal message protection asymptotic and non asymptotic tradeoffs"
    - "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)