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: "streaming data transmission in the moderate deviations and central limit regimes"
  Abstract: We consider streaming data transmission over a discrete memoryless channel. A new message is given to the encoder at the beginning of each block and the decoder decodes each message sequentially, after a delay of $T$ blocks. In this streaming setup, we study the fundamental interplay between the rate and error probability in the central limit and moderate deviations regimes and show that i) in the moderate deviations regime, the moderate deviations constant improves over the block coding or non-streaming setup by a factor of $T$ and ii) in the central limit regime, the second-order coding rate improves by a factor of approximately $\sqrt{T}$ for a wide range of channel parameters. For both regimes, we propose coding techniques that incorporate a joint encoding of fresh and previous messages. In particular, for the central limit regime, we propose a coding technique with truncated memory to ensure that a summation of constants, which arises as a result of applications of the central limit theorem, does not diverge in the error analysis. #R##N#Furthermore, we explore interesting variants of the basic streaming setup in the moderate deviations regime. We first consider a scenario with an erasure option at the decoder and show that both the exponents of the total error and the undetected error probabilities improve by factors of $T$. Next, by utilizing the erasure option, we show that the exponent of the total error probability can be improved to that of the undetected error probability (in the order sense) at the expense of a variable decoding delay. Finally, we also extend our results to the case where the message rate is not fixed but alternates between two values.

Connected through hyperedge HE-1 (induced by source paper "moderate deviation asymptotics for variable length codes with feedback"):
  with members:
    - "extrinsic jensen shannon divergence applications to variable length coding"
    - "asymmetric evaluations of erasure and undetected error probabilities"
    - "moderate deviations in channel coding"
    - "moderate deviations of lossy source coding for discrete and gaussian sources"
    - "opportunistic capacity and error exponent regions for compound channel with feedback"
    - "optimal feedback communication via posterior matching"
    - "a simple converse of burnashev s reliability function"
    - "error exponents for variable length block codes with feedback and cost constraints"

Connected through hyperedge HE-2 (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"
    - "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"
    - "moderate deviations of lossy source coding for discrete and gaussian sources"

Connected through hyperedge HE-3 (induced by source paper "exact moderate deviation asymptotics in streaming data transmission"):
  with members:
    - "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)