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 based on partial information"):
  Abstract: We consider the decoding of linear and array codes from errors when we are only allowed to download a part of the codeword. More specifically, suppose that we have encoded $k$ data symbols using an $(n,k)$ code with code length $n$ and dimension $k.$ During storage, some of the codeword coordinates might be corrupted by errors. We aim to recover the original data by reading the corrupted codeword with a limit on the transmitting bandwidth, namely, we can only download an $\alpha$ proportion of the corrupted codeword. For a given $\alpha,$ our objective is to design a code and a decoding scheme such that we can recover the original data from the largest possible number of errors. A naive scheme is to read $\alpha n$ coordinates of the codeword. This method used in conjunction with MDS codes guarantees recovery from any $\lfloor(\alpha n-k)/2\rfloor$ errors. In this paper we show that we can instead read an $\alpha$ proportion from each of the codeword's coordinates. For a well-designed MDS code, this method can guarantee recovery from $\lfloor (n-k/\alpha)/2 \rfloor$ errors, which is $1/\alpha$ times more than the naive method, and is also the maximum number of errors that an $(n,k)$ code can correct by downloading only an $\alpha$ proportion of the codeword. We present two families of such optimal constructions and decoding schemes. One is a Reed-Solomon code with evaluation points in a subfield and the other is based on Folded Reed-Solomon codes. We further show that both code constructions attain asymptotically optimal list decoding radius when downloading only a part of the corrupted codeword. We also construct an ensemble of random codes that with high probability approaches the upper bound on the number of correctable errors when the decoder downloads an $\alpha$ proportion of the corrupted codeword.

Includes members:
  Member "on fractional decoding of reed solomon codes":
    also connected through other hyperedges:
      (none)
  Member "cooperative repair constructions of optimal mds codes for all admissible parameters":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "topology aware cooperative data protection in blockchain based decentralized storage networks")
  Member "fractional decoding error correction from partial information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on error decoding of locally repairable and partial mds codes")
  Member "optimal repair of reed solomon codes achieving the cut set bound":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an exponential lower bound on the sub packetization of msr codes")
  Member "explicit constructions of high rate mds array codes with optimal repair bandwidth":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards practical private information retrieval from mds array codes")
  Member "explicit constructions of optimal access mds codes with nearly optimal sub packetization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards practical private information retrieval from mds array codes")
  Member "repairing reed solomon codes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "enabling optimal access and error correction for the repair of reed solomon codes")
  Member "zigzag codes mds array codes with optimal rebuilding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "enabling optimal access and error correction for the repair of reed solomon 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)