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: "improved list decodability of random linear binary codes"
  Abstract: There has been a great deal of work establishing that random linear codes are as list-decodable as uniformly random codes, in the sense that a random linear binary code of rate $1 - H(p) - \epsilon$ is $(p,O(1/\epsilon))$-list-decodable with high probability. In this work, we show that such codes are $(p, H(p)/\epsilon + 2)$-list-decodable with high probability, for any $p \in (0, 1/2)$ and $\epsilon > 0$. In addition to improving the constant in known list-size bounds, our argument, which is quite simple, works simultaneously for all values of $p$, while previous works obtaining $L = O(1/\epsilon)$ patched together different arguments to cover different parameter regimes. #R##N#Our approach is to strengthen an existential argument of (Guruswami, H{\aa}stad, Sudan and Zuckerman, IEEE Trans. IT, 2002) to hold with high probability. To complement our upper bound for random linear codes, we also improve an argument of (Guruswami, Narayanan, IEEE Trans. IT, 2014) to obtain an essentially tight lower bound of $1/\epsilon$ on the list size of uniformly random codes; this implies that random linear codes are in fact more list-decodable than uniformly random codes, in the sense that the list sizes are strictly smaller. #R##N#To demonstrate the applicability of these techniques, we use them to (a) obtain more information about the distribution of list sizes of random linear codes and (b) to prove a similar result for random linear rank-metric codes.

Connected through hyperedge HE-1 (induced by source paper "ldpc codes achieve list decoding capacity"):
  with members:
    - "improved decoding of folded reed solomon and multiplicity codes"
    - "linear time list recovery of high rate expander codes"
    - "analysis of boolean functions"
    - "every list decodable code for high noise has abundant near optimal rate puncturings"
    - "on the list decodability of random linear codes with large error rates"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "folded codes from function field towers and improved optimal rate list decoding"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"

Connected through hyperedge HE-2 (induced by source paper "list decoding random euclidean codes and infinite constellations"):
  with members:
    - "good distance lattices from high dimensional expanders"
    - "quadratically constrained myopic adversarial channels"
    - "on the weight distribution of random binary linear codes"
    - "list decoding with double samplers"
    - "sufficiently myopic adversaries are blind"
    - "it ll probably work out improved list decoding through random operations"
    - "every list decodable code for high noise has abundant near optimal rate puncturings"
    - "on the list decodability of random linear codes with large error rates"

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)