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 "error correction by structural simplicity correcting samplable additive errors"):
  Abstract: This paper explores the possibilities and limitations of error correction by the structural simplicity of error mechanisms. Specifically, we consider channel models, called \emph{samplable additive channels}, in which (a) errors are efficiently sampled without the knowledge of the coding scheme or the transmitted codeword; (b) the entropy of the error distribution is bounded; and (c) the number of errors introduced by the channel is unbounded. For the channels, several negative and positive results are provided. Assuming the existence of one-way functions, there are samplable additive errors of entropy $n^{\epsilon}$ for $\epsilon \in (0,1)$ that are pseudorandom, and thus not correctable by efficient coding schemes. It is shown that there is an oracle algorithm that induces a samplable distribution over $\{0,1\}^n$ of entropy $m = \omega( \log n)$ that is not pseudorandom, but is uncorrectable by efficient schemes of rate less than $1 - m/n - o(1)$. The results indicate that restricting error mechanisms to be efficiently samplable and not pseudorandom is insufficient for error correction. As positive results, some conditions are provided under which efficient error correction is possible.

Includes members:
  Member "non malleable coding against bit wise and split state tampering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "leakage resilient non malleable secret sharing in non compartmentalized models")
  Member "optimal rate code constructions for computationally simple channels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "locally decodable codes with randomized encoding")

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)