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: "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.

Connected through hyperedge HE-1 (induced by source paper "an overview of capacity results for synchronization channels"):
  with members:
    - "coding for deletion channels with multiple traces"
    - "coded trace reconstruction"
    - "coded trace reconstruction in a constant number of traces"
    - "zero error capacity of duplication channels"
    - "approximating edit distance within constant factor in truly sub quadratic time"
    - "maximum likelihood upper bounds on the capacities of discrete information stable channels"
    - "sharp analytical capacity upper bounds for sticky and related channels"
    - "on the list decodability of insertions and deletions"

Connected through hyperedge HE-2 (induced by source paper "covering codes for insertions and deletions"):
  with members:
    - "an overview of capacity results for synchronization channels"
    - "near linear time insertion deletion codes and 1 e approximating edit distance via indexing"
    - "polynomial time decodable codes for the binary deletion channel"
    - "an improved bound on the fraction of correctable deletions"
    - "locality sensitive hashing without false negatives"
    - "coding for interactive communication correcting insertions and deletions"
    - "generalized sphere packing bound"
    - "a characterization of the number of subsequences obtained via the deletion channel"

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)