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 "synchronization strings list decoding for insertions and deletions"):
  Abstract: We study codes that are list-decodable under insertions and deletions. Specifically, we consider the setting where a codeword over some finite alphabet of size $q$ may suffer from $\delta$ fraction of adversarial deletions and $\gamma$ fraction of adversarial insertions. A code is said to be $L$-list-decodable if there is an (efficient) algorithm that, given a received word, reports a list of $L$ codewords that include the original codeword. #R##N#Using the concept of synchronization strings, introduced by the first two authors [STOC 2017], we show some surprising results. We show that for every $0\leq\delta 0$ there exist efficient codes of rate $1-\delta-\epsilon$ and constant alphabet (so $q=O_{\delta,\gamma,\epsilon}(1)$) and sub-logarithmic list sizes. We stress that the fraction of insertions can be arbitrarily large and the rate is independent of this parameter. Our result sheds light on the remarkable asymmetry between the impact of insertions and deletions from the point of view of error-correction: Whereas deletions cost in the rate of the code, insertion costs are borne by the adversary and not the code! #R##N#We also prove several tight bounds on the parameters of list-decodable insdel codes. In particular, we show that the alphabet size of insdel codes needs to be exponentially large in $\epsilon^{-1}$, where $\epsilon$ is the gap to capacity above. Our result even applies to settings where the unique-decoding capacity equals the list-decoding capacity and when it does so, it shows that the alphabet size needs to be exponentially large in the gap to capacity. This is sharp contrast to the Hamming error model where alphabet size polynomial in $\epsilon^{-1}$ suffices for unique decoding and also shows that the exponential dependence on the alphabet size in previous works that constructed insdel codes is actually necessary!

Includes members:
  Member "list decoding of insertions and deletions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimally resilient codes for list decoding from insertions and deletions")
  Member "synchronization strings channel simulations and interactive coding for insertions and deletions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "locally decodable codes with randomized encoding")
  Member "synchronization strings explicit constructions local decoding and applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "locally decodable codes with randomized encoding")
  Member "local list recovery of high rate tensor codes and applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "linear time erasure list decoding of expander codes")
  Member "optimal rate list decoding over bounded alphabets using algebraic geometric codes":
    also connected through other hyperedges:
      (none)
  Member "synchronization strings codes for insertions and deletions approaching the singleton bound":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "locally decodable codes with randomized encoding")
  Member "linear time list recovery of high rate expander codes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "linear time erasure list decoding of expander 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)