human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "exact moderate deviation asymptotics in streaming data transmission":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "achievable moderate deviations asymptotics for streaming compression of correlated sources")
  Member "asymptotic estimates in information theory with non vanishing error probabilities":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "finite sample analysis of image registration")
  Member "on error exponents and moderate deviations for lossless streaming compression of correlated sources":
    also connected through other hyperedges:
      (none)
  Member "convolutional codes with maximum column sum rank for network streaming":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "locally repairable convolutional codes with sliding window repair")
  Member "asymmetric evaluations of erasure and undetected error probabilities":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed detection with empirically observed statistics")
  Member "unequal message protection asymptotic and non asymptotic tradeoffs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "universal random access error exponents for codebooks of different word lengths")
  Member "the streaming dmt of fading channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generalized harq protocols with delayed channel state information and average latency constraints")
  Member "moderate deviations in channel coding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "optimal resolution of change point detection with empirically observed statistics and erasures")

Question: Please tell me which class the source paper 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)