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 "it ll probably work out improved list decoding through random operations"):
  Abstract: In this work, we introduce a framework to study the effect of random operations on the combinatorial list-decodability of a code. The operations we consider correspond to row and column operations on the matrix obtained from the code by stacking the codewords together as columns. This captures many natural transformations on codes, such as puncturing, folding, and taking subcodes; we show that many such operations can improve the list-decoding properties of a code. There are two main points to this. First, our goal is to advance our (combinatorial) understanding of list-decodability, by understanding what structure (or lack thereof) is necessary to obtain it. Second, we use our more general results to obtain a few interesting corollaries for list decoding: #R##N#(1) We show the existence of binary codes that are combinatorially list-decodable from $1/2-\epsilon$ fraction of errors with optimal rate $\Omega(\epsilon^2)$ that can be encoded in linear time. #R##N#(2) We show that any code with $\Omega(1)$ relative distance, when randomly folded, is combinatorially list-decodable $1-\epsilon$ fraction of errors with high probability. This formalizes the intuition for why the folding operation has been successful in obtaining codes with optimal list decoding parameters; previously, all arguments used algebraic methods and worked only with specific codes. #R##N#(3) We show that any code which is list-decodable with suboptimal list sizes has many subcodes which have near-optimal list sizes, while retaining the error correcting capabilities of the original code. This generalizes recent results where subspace evasive sets have been used to reduce list sizes of codes that achieve list decoding capacity.

Includes members:
  Member "every list decodable code for high noise has abundant near optimal rate puncturings":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on error decoding of locally repairable and partial mds codes")
  Member "on the list decodability of random linear codes with large error rates":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generalized list decoding")
  Member "combinatorial limitations of average radius list decoding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalized list decoding")
  Member "folded codes from function field towers and improved optimal rate list decoding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "linear time erasure list decoding of expander codes")
  Member "restricted isometry of fourier matrices and list decodability of random linear codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "one shot neural architecture search via compressive sensing")
  Member "linear algebraic list decoding of folded reed solomon codes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "beyond the guruswami sudan and parvaresh vardy radii folded reed solomon multiplicity and derivative codes")
  Member "subspace evasive sets":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "beyond the guruswami sudan and parvaresh vardy radii folded reed solomon multiplicity and derivative codes")
  Member "list decoding tensor products and interleaved codes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "new lower bounds on the generalized hamming weights of ag codes")

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)