human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "an overview of capacity results for synchronization channels"):
  with members:
    - "coding for deletion channels with multiple traces"
    - "coded trace reconstruction"
    - "coded trace reconstruction in a constant number of traces"
    - "optimally resilient codes for list decoding from insertions and deletions"
    - "zero error capacity of duplication channels"
    - "approximating edit distance within constant factor in truly sub quadratic time"
    - "maximum likelihood upper bounds on the capacities of discrete information stable channels"
    - "sharp analytical capacity upper bounds for sticky and related channels"

Connected through hyperedge HE-2 (induced by source paper "coded trace reconstruction in a constant number of traces"):
  with members:
    - "coding for deletion channels with multiple traces"
    - "coded trace reconstruction"
    - "beyond trace reconstruction population recovery from the deletion channel"
    - "trace reconstruction generalized and parameterized"
    - "unique reconstruction of coded strings from multiset substring spectra"
    - "average case reconstruction for the deletion channel subpolynomially many traces suffice"
    - "polynomial time decodable codes for the binary deletion channel"
    - "synchronization strings explicit constructions local decoding and applications"

Connected through hyperedge HE-3 (induced by source paper "polar codes for the deletion channel weak and strong polarization"):
  with members:
    - "universal polarization for processes with memory"
    - "a simple proof of fast polarization"
    - "fast polarization for processes with memory"
    - "polar coding for the binary erasure channel with deletions"
    - "polar coding for processes with memory"
    - "joint successive cancellation decoding of polar codes over intersymbol interference channels"
    - "list decoding of polar codes"
    - "polar codes characterization of exponent bounds and constructions"

Connected through hyperedge HE-4 (induced by source paper "explicit and efficient constructions of coding schemes for the binary deletion channel and the poisson repeat channel"):
  with members:
    - "polynomial time decodable codes for the binary deletion channel"
    - "synchronization strings codes for insertions and deletions approaching the singleton bound"
    - "an improved bound on the fraction of correctable deletions"
    - "deletion codes in the high noise and high rate regimes"
    - "a new bound for the capacity of the deletion channel with high deletion probabilities"

Connected through hyperedge HE-5 (induced by source paper "tutorial on algebraic deletion correction codes"):
  with members:
    - "codes correcting two deletions"
    - "efficient systematic encoding of non binary vt codes"
    - "codes correcting a burst of deletions or insertions"
    - "efficient low redundancy codes for correcting multiple deletions"

Connected through hyperedge HE-6 (induced by source paper "sharp analytical capacity upper bounds for sticky and related channels"):
  with members:
    - "optimal document exchange and new codes for small number of insertions and deletions"
    - "on the capacity of channels with timing synchronization errors"
    - "bounds on the capacity of random insertion and deletion additive noise channels"
    - "achievable rates for channels with deletions and insertions"
    - "achievable rates for noisy channels with synchronization errors"
    - "novel bounds on the capacity of the binary deletion channel"

Connected through hyperedge HE-7 (induced by source paper "maximum likelihood upper bounds on the capacities of discrete information stable channels"):
  with members:
    - "a characterization of the number of subsequences obtained via the deletion channel"
    - "novel bounds on the capacity of the binary deletion channel"
    - "the capacity of channels with feedback"

Connected through hyperedge HE-8 (induced by source paper "from clustering supersequences to entropy minimizing subsequences for single and double deletions"):
  with members:
    - "bounds on the capacity of random insertion and deletion additive noise channels"
    - "an improvement to levenshtein s upper bound on the cardinality of deletion correcting codes"
    - "a characterization of the number of subsequences obtained via the deletion channel"

Question: Please tell me which class the center node 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)