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 "correction of samplable additive errors"):
  Abstract: We study the correctability of efficiently samplable errors. Specifically, we consider the setting in which errors are efficiently samplable without the knowledge of the code or the transmitted codeword, and the error rate is not bounded. It is shown that for every flat distribution $Z$ over $\{0,1\}^n$ with support of size $2^m$, there is a code that corrects $Z$ with optimal rate $1 - m/n$ and the decoding complexity $O(n^32^m)$. If the support of $Z$ forms a linear subspace, there is a linear code that corrects $Z$ with rate $1 - m/n$ by polynomial-time decoding. We show that there is an oracle relative to which there is a samplable flat distribution $Z$ that is not pseudorandom, but uncorrectable by polynomial-time coding schemes of rate $1 - m/n - \omega(\log n/n)$. The result implies the difficulty of correcting every samplable distribution $Z$ even when $Z$ is not pseudorandom. Finally, we show that the existence of one-way functions is necessary to derive impossibility results for coding schemes of rate less than $1 - m/n$.

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")
  Member "capacity achieving codes from randomness condensers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sub linear time support recovery for compressed sensing using sparse graph 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)