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 "good random matrices over finite fields"):
  Abstract: The random matrix uniformly distributed over the set of all#R##N#  $m$-by-$n$ matrices over a finite field plays an important role in#R##N#  many branches of information theory. In this paper a generalization#R##N#  of this random matrix, called $k$-good random matrices, is#R##N#  studied. It is shown that a $k$-good random $m$-by-$n$ matrix with a#R##N#  distribution of minimum support size is uniformly distributed over a#R##N#  maximum-rank-distance (MRD) code of minimum rank distance#R##N#  min{$m,n$}$-k+1$, and vice versa. Further examples of $k$-good#R##N#  random matrices are derived from homogeneous weights on matrix#R##N#  modules. Several applications of $k$-good random matrices are given,#R##N#  establishing links with some well-known combinatorial problems.#R##N#  Finally, the related combinatorial concept of a $k$-dense set of#R##N#  $m$-by-$n$ matrices is studied, identifying such sets as blocking#R##N#  sets with respect to $(m-k)$-dimensional flats in a certain#R##N#  $m$-by-$n$ matrix geometry and determining their minimum size in#R##N#  special cases.

Includes members:
  Member "constructing linear encoders with good spectra":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "weight distributions of regular low density parity check codes over finite fields")
  Member "linear codes based lossless joint source channel coding for multiple access channels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "constructing linear encoders with good spectra")

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)