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 "coding theorem and converse for abstract channels with time structure and memory"):
  Abstract: A coding theorem and converse are proved for a large class of abstract stationary channels with time structure including the result by Kadota and Wyner (1972) on continuous-time real-valued channels as special cases. As main contribution the coding theorem is proved for a significantly weaker condition on the channel output memory - called total ergodicity w.r.t. finite alphabet block-memoryless input sources - and under a crucial relaxation of the measurability requirement for the channel. These improvements are achieved by introducing a suitable characterization of information rate capacity. It is shown that the $\psi$-mixing output memory condition used by Kadota and Wyner is quite restrictive and excludes important channel models, in particular for the class of Gaussian channels. In fact, it is proved that for Gaussian (e.g., fading or additive noise) channels the $\psi$-mixing condition is equivalent to finite output memory. Further, it is demonstrated that the measurability requirement of Kadota and Wyner is not satisfied for relevant continuous-time channel models such as linear filters, whereas the condition used in this paper is satisfied for these models. Moreover, a weak converse is derived for all stationary channels with time structure. Intersymbol interference as well as input constraints are taken into account in a general and flexible way, including amplitude and average power constraints as special case. Formulated in rigorous mathematical terms complete, explicit, and transparent proofs are presented. As a side product a gap in the proof of Kadota and Wyner - illustrated by a counterexample - is closed by providing a corrected proof of a lemma on the monotonicity of some sequence of normalized mutual information quantities. An abstract framework is established to treat discrete- and continuous-time channels with memory and arbitrary alphabets in a unified way.

Includes members:
  Member "the szeg o asymptotics for doubly dispersive gaussian channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "waterfilling theorems in the time frequency plane for the heat channel and a related source")
  Member "directed information causal estimation and communication in continuous time":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generic variance bounds on estimation and prediction errors in time series analysis an entropy perspective")
  Member "on the sensitivity of continuous time noncoherent fading channel capacity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "off the grid recovery of time and frequency shifts with multiple measurement vectors")
  Member "noncoherent capacity of underspread fading channels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "interference cancellation and iterative detection for orthogonal time frequency space modulation")
  Member "on multipath fading channels at high snr":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the capacity of a class of signal dependent noise channels")

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)