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 "asynchronous communication over a fading channel and additive noise"):
  Abstract: In \cite{Chandar2008}, Chandar et al studied a problem of sequential frame synchronization for a frame transmitted randomly and uniformly among $A$ slots. For a discrete memory-less channel (DMC), they showed that the frame length $N$ must scale as $e^{\alpha(Q)N}>A$ for the frame detection error to go to zero asymptotically with $A$. $\alpha(Q)$ is the synchronization threshold and $Q$ is channel transition probability. We study the sequential frame synchronisation problem for a fading channel and additive noise and seek to characterise the effect of fading. For a discrete ON-OFF fading channel (with ON probability $p$) and additive noise (with channel transition probabilities $Q_n$), we characterise the synchronisation threshold of the composite channel $\alpha(Q)$ and show that $\alpha(Q)\leq p\,\alpha(Q_n)$. We then characterize the synchronization threshold for Rayleigh fading and AWGN channel as a function of channel parameters. The asynchronous framework permits a trade-off between sync frame length, $N$, and channel, $Q$, to support asynchronism. This allows us to characterize the synchronization threshold with sync frame energy instead of sync frame length.

Includes members:
  Member "communication under strong asynchronism":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the reliability function of distributed hypothesis testing under optimal detection")
  Member "optimal sequential frame synchronization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the strongly asynchronous massive access channel")

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)