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 "causal erasure channels"):
  Abstract: We consider the communication problem over binary causal adversarial erasure channels. Such a channel maps $n$ input bits to $n$ output symbols in $\{0,1,\wedge\}$, where $\wedge$ denotes erasure. The channel is causal if, for every $i$, the channel adversarially decides whether to erase the $i$th bit of its input based on inputs $1,...,i$, before it observes bits $i+1$ to $n$. Such a channel is $p$-bounded if it can erase at most a $p$ fraction of the input bits over the whole transmission duration. Causal channels provide a natural model for channels that obey basic physical restrictions but are otherwise unpredictable or highly variable. For a given erasure rate $p$, our goal is to understand the optimal rate (the capacity) at which a randomized (stochastic) encoder/decoder can transmit reliably across all causal $p$-bounded erasure channels. In this paper, we introduce the causal erasure model and provide new upper bounds and lower bounds on the achievable rate. Our bounds separate the achievable rate in the causal erasures setting from the rates achievable in two related models: random erasure channels (strictly weaker) and fully adversarial erasure channels (strictly stronger). Specifically, we show: #R##N#- A strict separation between random and causal erasures for all constant erasure rates $p\in(0,1)$. #R##N#- A strict separation between causal and fully adversarial erasures for $p\in(0,\phi)$ where $\phi \approx 0.348$. #R##N#- For $p\in[\phi,1/2)$, we show codes for causal erasures that have higher rate than the best known constructions for fully adversarial channels. #R##N#Our results contrast with existing results on correcting causal bit-flip errors (as opposed to erasures) [Dey et. al 2008, 2009], [Haviv-Langberg 2011]. For the separations we provide, the analogous separations for bit-flip models are either not known at all or much weaker.

Includes members:
  Member "beating the gilbert varshamov bound for online channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sufficiently myopic adversaries are blind")
  Member "binary causal adversary channels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sufficiently myopic adversaries are blind")
  Member "codes against online adversaries":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sufficiently myopic adversaries are blind")

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)