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 "several classes of two weight and three weight linear codes"):
  Abstract: Linear codes with few weights have been widely studied due to their applications in secret sharing, authentication codes, association schemes and strongly regular graphs. In this paper, we further construct several classes of new two-weight and three-weight linear codes from defining sets and determine their weight distributions by applications of the theory of quadratic forms and Weil sums over finite fields. Some of the linear codes obtained are optimal or almost optimal with respect to the Griesmer bound. This paper generalizes some results in~\cite{Jian2019,LiYueFu2016}.

Includes members:
  Member "two weight and three weight linear codes based on weil sums":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "weight hierarchies and weight distributions of two families of p ary linear codes")
  Member "a construction of binary linear codes from boolean functions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "minimal linear codes constructed from functions")
  Member "linear codes from some 2 designs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weight distributions and weight hierarchies of two classes of binary linear codes")
  Member "linear codes with two or three weights from quadratic bent functions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "two weight and three weight linear codes based on weil sums")
  Member "a class of three weight and five weight linear codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a construction of linear codes and their complete weight enumerators")
  Member "linear codes with two or three weights from weakly regular bent functions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "minimal linear codes constructed from functions")
  Member "a class of two weight and three weight codes and their applications in secret sharing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "weight distributions and weight hierarchies of two classes of binary linear codes")
  Member "a class of three weight cyclic codes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a class of narrow sense bch 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)