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 "on list decoding of insertion and deletion errors"):
  Abstract: Insertion and deletion (Insdel for short) errors are synchronization errors in communication systems caused by the loss of positional information of the message. Since the work by Guruswami and Wang [13] that studied list decoding of binary codes with deletion errors only, there have been some further investigations on the list decoding of insertion codes, deletion codes and insdel codes. However, unlike classical Hamming metric or even rank-metric, there are still many unsolved problems on list decoding of insdel codes. #R##N#The purpose of the current paper is to move toward complete or partial solutions for some of these problems. Our contributions mainly consist of three parts. Firstly, we provide an upper bound on the list decoding radius of an insdel code in terms of its rate. This bound provides some improvements when degenerated to insertions only and deletions only compared to the previous results in [18]. Secondly, we analyse the list decodability of random insdel codes. It shows that although there is a gap between the list decoding radius of random insdel codes and our upper bound on list decoding radius, when the alphabet size is sufficiently large, this gap no longer exists. In addition, we show that list decoding of random insdel codes surpasses the Singleton bound when there are more insertion errors than deletion errors and the alphabet size is sufficiently large. We also find that our results improve some previous findings in [18] and [13]. Furthermore, our results reveal the existence of an insdel code that can be list decoded against insdel errors beyond its minimum insdel distance while still having polynomial list size. Lastly, we construct a family of explicit insdel codes with efficient list decoding algorithm. As a result, we obtain a Zyablov-type bound for insdel errors.

Includes members:
  Member "near linear time insertion deletion codes and 1 e approximating edit distance via indexing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximate similarity search under edit distance using locality sensitive hashing")
  Member "on the list decodability of insertions and deletions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an overview of capacity results for synchronization channels")
  Member "synchronization strings list decoding for insertions and deletions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an overview of capacity results for synchronization channels")
  Member "list decoding of insertions and deletions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimally resilient codes for list decoding from insertions and deletions")
  Member "bounds on codes correcting tandem and palindromic duplications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "uncertainty of reconstruction with list decoding from uniform tandem duplication noise")
  Member "synchronization strings codes for insertions and deletions approaching the singleton bound":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "locally decodable codes with randomized encoding")
  Member "efficiently decodable insertion deletion codes for high noise and high rate regimes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "locally decodable codes with randomized encoding")
  Member "codes correcting a burst of deletions or insertions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "optimal codes correcting a burst of deletions of variable length")

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)