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: "on the list decodability of random linear codes with large error rates"
  Abstract: It is well known that a random q-ary code of rate \Omega(\epsilon^2) is list decodable up to radius (1 - 1/q - \epsilon) with list sizes on the order of 1/\epsilon^2, with probability 1 - o(1). However, until recently, a similar statement about random linear codes has until remained elusive. In a recent paper, Cheraghchi, Guruswami, and Velingker show a connection between list decodability of random linear codes and the Restricted Isometry Property from compressed sensing, and use this connection to prove that a random linear code of rate \Omega(\epsilon^2 / log^3(1/\epsilon)) achieves the list decoding properties above, with constant probability. We improve on their result to show that in fact we may take the rate to be \Omega(\epsilon^2), which is optimal, and further that the success probability is 1 - o(1), rather than constant. As an added benefit, our proof is relatively simple. Finally, we extend our methods to more general ensembles of linear codes. As an example, we show that randomly punctured Reed-Muller codes have the same list decoding properties as the original codes, even when the rate is improved to a constant.

Connected through hyperedge HE-1 (induced by source paper "generalized list decoding"):
  with members:
    - "list decodable linear regression"
    - "list decodable learning via sum of squares"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "list decodable zero rate codes"
    - "an improved bound on the fraction of correctable deletions"
    - "upper bound on list decoding radius of binary codes"
    - "a characterization of the capacity of online causal binary channels"
    - "it ll probably work out improved list decoding through random operations"

Connected through hyperedge HE-2 (induced by source paper "ldpc codes achieve list decoding capacity"):
  with members:
    - "improved list decodability of random linear binary codes"
    - "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"
    - "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-3 (induced by source paper "list decoding random euclidean codes and infinite constellations"):
  with members:
    - "good distance lattices from high dimensional expanders"
    - "improved list decodability of random linear binary codes"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "combinatorial list decoding of reed solomon codes beyond the johnson radius"):
  with members:
    - "every list decodable code for high noise has abundant near optimal rate puncturings"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"
    - "on the list decodability of random linear codes"
    - "explicit codes achieving list decoding capacity error correction with optimal redundancy"

Connected through hyperedge HE-5 (induced by source paper "improved list decodability of random linear binary codes"):
  with members:
    - "on the list decodability of random linear rank metric codes"
    - "linear time list recovery of high rate expander codes"
    - "it ll probably work out improved list decoding through random operations"
    - "optimal locally repairable codes via rank metric codes"
    - "bounds on list decoding of rank metric codes"
    - "every list decodable code for high noise has abundant near optimal rate puncturings"
    - "error resilience in distributed storage via rank metric codes"
    - "folded codes from function field towers and improved optimal rate list decoding"

Connected through hyperedge HE-6 (induced by source paper "on the list decodability of random linear rank metric codes"):
  with members:
    - "average radius list recovery of random linear codes it really ties the room together"
    - "some gabidulin codes cannot be list decoded efficiently at any radius"
    - "every list decodable code for high noise has abundant near optimal rate puncturings"
    - "bounds on list decoding gabidulin codes"
    - "on identity testing of tensors low rank recovery and compressed sensing"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"
    - "on the list decodability of random linear codes"
    - "a rank metric approach to error control in random network coding"

Connected through hyperedge HE-7 (induced by source paper "average radius list recovery of random linear codes it really ties the room together"):
  with members:
    - "linear time list recovery of high rate expander codes"
    - "it ll probably work out improved list decoding through random operations"
    - "on the list decodability of random linear codes"

Connected through hyperedge HE-8 (induced by source paper "on the list decodability of random self orthogonal codes"):
  with members:
    - "on the list decodability of random linear codes"

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)