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 zero error capacity threshold for deletion channels"
  Abstract: We consider the zero-error capacity of deletion channels. Specifically, we consider the setting where we choose a codebook ${\cal C}$ consisting of strings of $n$ bits, and our model of the channel corresponds to an adversary who may delete up to $pn$ of these bits for a constant $p$. Our goal is to decode correctly without error regardless of the actions of the adversary. We consider what values of $p$ allow non-zero capacity in this setting. We suggest multiple approaches, one of which makes use of the natural connection between this problem and the problem of finding the expected length of the longest common subsequence of two random sequences.

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"
    - "optimally resilient codes for list decoding from insertions and deletions"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "polynomial time decodable codes for the binary deletion channel"):
  with members:
    - "optimal document exchange and new codes for small number of insertions and deletions"
    - "deterministic document exchange protocols and almost optimal binary codes for edit errors"
    - "capacity upper bounds for deletion type channels"
    - "optimal document exchange and new codes for insertions and deletions"
    - "synchronization strings codes for insertions and deletions approaching the singleton bound"
    - "efficiently decodable insertion deletion codes for high noise and high rate regimes"
    - "coding against deletions in oblivious and online models"
    - "an improved bound on the fraction of correctable deletions"

Connected through hyperedge HE-3 (induced by source paper "coding against deletions in oblivious and online models"):
  with members:
    - "efficiently decodable insertion deletion codes for high noise and high rate regimes"
    - "an improved bound on the fraction of correctable deletions"
    - "a characterization of the capacity of online causal binary channels"
    - "causal erasure channels"
    - "upper bounds on the capacity of binary channels with causal adversaries"
    - "optimal rate code constructions for computationally simple channels"

Connected through hyperedge HE-4 (induced by source paper "deletion codes in the high noise and high rate regimes"):
  with members:
    - "efficiently decodable insertion deletion codes for high noise and high rate regimes"
    - "an improved bound on the fraction of correctable deletions"
    - "efficient low redundancy codes for correcting multiple deletions"
    - "generalized sphere packing bound"
    - "non asymptotic upper bounds for deletion correcting codes"
    - "explicit codes achieving list decoding capacity error correction with optimal redundancy"

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)