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 "list decoding of subspace codes and rank metric codes up to singleton bound"):
  Abstract: Subspace codes and rank-metric codes can be used to correct errors and erasures in network, with linear network coding. Subspace codes were introduced by Koetter and Kschischang to correct errors and erasures in networks where topology is unknown (the noncoherent case). In a previous work, we have developed a family of subspace codes, based upon the Koetter-Kschichang construction, which are efficiently list decodable. Using these codes, we achieved a better decoding radius than Koetter-Kschischiang codes at low rates. Herein, we introduce a new family of subspace codes based upon a different approach which leads to a linear-algebraic list-decoding algorithm. The resulting error correction radius can be expressed as follows: for any integer $s$, our list-decoder using $s+1$-interpolation polynomials guarantees successful recovery of the message subspace provided the normalized dimension of errors is at most $s(1-sR)$. The same list-decoding algorithm can be used to correct erasures as well as errors. The size of output list is at most $Q^{s-1}$, where $Q$ is the size of the field that message symbols are chosen from. Rank-metric codes are suitable for error correction in the case where the network topology and the underlying network code are known (the coherent case). Gabidulin codes are a well-known class of algebraic rank-metric codes that meet the Singleton bound on the minimum rank metric of a code. In this paper, we introduce a folded version of Gabidulin codes analogous to the folded Reed-Solomon codes of Guruswami and Rudra along with a list-decoding algorithm for such codes. Our list-decoding algorithm makes it possible to recover the message provided that the normalized rank of error is at most $1-R-\epsilon$, for any $\epsilon > 0$. Notably this achieves the information theoretic bound on the decoding radius of a rank-metric code.

Includes members:
  Member "algebraic list decoding of subspace codes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "analog subspace coding a new approach to coding for non coherent wireless networks")
  Member "general linearized polynomial interpolation and its applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "list and probabilistic unique decoding of folded subspace codes")
  Member "linear algebraic list decoding of folded reed solomon codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "beyond the guruswami sudan and parvaresh vardy radii folded reed solomon multiplicity and derivative codes")
  Member "on metrics for error correction in network coding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "point line incidence on grassmannians and majority logic decoding of grassmann codes")
  Member "a rank metric approach to error control in random network coding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "roos bound for skew cyclic codes in hamming and rank metric")
  Member "coding for errors and erasures in random network coding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the gap between scalar and vector solutions of generalized combination networks")
  Member "explicit codes achieving list decoding capacity error correction with optimal redundancy":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "beyond the guruswami sudan and parvaresh vardy radii folded reed solomon multiplicity and derivative 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)