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 "optimally resilient codes for list decoding from insertions and deletions"):
  Abstract: We give a complete answer to the following basic question: "What is the maximal fraction of deletions or insertions tolerable by $q$-ary list-decodable codes with non-vanishing information rate?" #R##N#This question has been open even for binary codes, including the restriction to the binary insertion-only setting, where the best known results was that a $\gamma\leq 0.707$ fraction of insertions is tolerable by some binary code family. #R##N#For any desired $\epsilon > 0$, we construct a family of binary codes of positive rate which can be efficiently list-decoded from any combination of $\gamma$ fraction of insertions and $\delta$ fraction of deletions as long as $ \gamma+2\delta\leq 1-\epsilon$. On the other hand, for any $\gamma,\delta$ with $\gamma+2\delta=1$ list-decoding is impossible. Our result thus precisely characterizes the feasibility region of binary list-decodable codes for insertions and deletions. #R##N#We further generalize our result to codes over any finite alphabet of size $q$. Surprisingly, our work reveals that the feasibility region for $q>2$ is not the natural generalization of the binary bound above. We provide tight upper and lower bounds that precisely pin down the feasibility region, which turns out to have a $(q-1)$-piece-wise linear boundary whose $q$ corner-points lie on a quadratic curve. #R##N#The main technical work in our results is proving the existence of code families of sufficiently large size with good list-decoding properties for any combination of $\delta,\gamma$ within the claimed feasibility region. We achieve this via an intricate analysis of codes introduced by [Bukh, Ma; SIAM J. Discrete Math; 2014]. Finally we give a simple yet powerful concatenation scheme for list-decodable insertion-deletion codes which transforms any such (non-efficient) code family (with vanishing information rate) into an efficiently decodable code family with constant rate.

Includes members:
  Member "list decoding of insertion and deletion codes":
    also connected through other hyperedges:
      (none)
  Member "explicit constructions of two dimensional reed solomon codes in high insertion and deletion noise regime":
    also connected through other hyperedges:
      (none)
  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 "general strong polarization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reed muller codes theory and algorithms")
  Member "synchronization strings list decoding for insertions and deletions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an overview of capacity results for synchronization channels")
  Member "deterministic document exchange protocols and almost optimal binary codes for edit errors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "locally decodable codes with randomized encoding")
  Member "optimal document exchange and new codes for insertions and deletions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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)