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 "capacity upper bounds for deletion type channels"):
  Abstract: We develop a systematic approach, based on convex programming and real analysis, for obtaining upper bounds on the capacity of the binary deletion channel and, more generally, channels with i.i.d. insertions and deletions. Other than the classical deletion channel, we give a special attention to the Poisson-repeat channel introduced by Mitzenmacher and Drinea (IEEE Transactions on Information Theory, 2006). Our framework can be applied to obtain capacity upper bounds for any repetition distribution (the deletion and Poisson-repeat channels corresponding to the special cases of Bernoulli and Poisson distributions). Our techniques essentially reduce the task of proving capacity upper bounds to maximizing a univariate, real-valued, and often concave function over a bounded interval. The corresponding univariate function is carefully designed according to the underlying distribution of repetitions and the choices vary depending on the desired strength of the upper bounds as well as the desired simplicity of the function (e.g., being only efficiently computable versus having an explicit closed-form expression in terms of elementary, or common special, functions).    Among our results, we show that the capacity of the binary deletion channel with deletion probability d is at most (1−d) logϕ for d ≥ 1/2, and, assuming the capacity function is convex, is at most 1−d log(4/ϕ) for d   Furthermore, we derive the first set of capacity upper bounds for the Poisson-repeat channel. Our results uncover further striking connections between this channel and the deletion channel, and suggest, somewhat counter-intuitively, that the Poisson-repeat channel is actually analytically simpler than the deletion channel and may be of key importance to a complete understanding of the deletion channel.    Finally, we derive several novel upper bounds on the capacity of the deletion channel. All upper bounds are maximums of efficiently computable, and concave, univariate real functions over a bounded domain. In turn, we upper bound these functions in terms of explicit elementary and standard special functions, whose maximums can be found even more efficiently (and sometimes, analytically, for example for d=1/2).    Along the way, we develop several new techniques of potentially independent interest. For example, we develop systematic techniques to study channels with mean constraints over the reals. Furthermore, we motivate the study of novel probability distributions over non-negative integers, as well as novel special functions which could be of interest to mathematical analysis.

Includes members:
  Member "sharp analytical capacity upper bounds for sticky and related channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an overview of capacity results for synchronization channels")
  Member "expressions for the entropy of binomial type distributions":
    also connected through other hyperedges:
      (none)
  Member "edit distance sketching streaming and document exchange":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "streaming with oracle new streaming algorithms for edit distance and lcs")
  Member "coding against deletions in oblivious and online models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an overview of capacity results for synchronization channels")
  Member "an improved bound on the fraction of correctable deletions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "locally decodable codes with randomized encoding")
  Member "efficient low redundancy codes for correcting multiple deletions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimal codes correcting a burst of deletions of variable length")
  Member "deletion codes in the high noise and high rate regimes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "locally decodable codes with randomized encoding")
  Member "list decoding subspace codes from insertions and deletions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "analog subspace coding a new approach to coding for non coherent wireless networks")

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)