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 "linear size optimal q ary constant weight codes and constant composition codes"):
  Abstract: An optimal constant-composition or constant-weight code of weight w has linear size if and only if its distance d is at least 2w-1. When d ? 2w, the determination of the exact size of such a constant-composition or constant-weight code is trivial, but the case of d=2w-1 has been solved previously only for binary and ternary constant-composition and constant-weight codes, and for some sporadic instances. This paper provides a construction for quasicyclic optimal constant-composition and constant-weight codes of weight w and distance 2w-1 based on a new generalization of difference triangle sets. As a result, the sizes of optimal constant-composition codes and optimal constant-weight codes of weight w and distance 2w-1 are determined for all such codes of sufficiently large lengths. This solves an open problem of Etzion. The sizes of optimal constant-composition codes of weight w and distance 2w-1 are also determined for all w ? 6, except in two cases.

Includes members:
  Member "group divisible codes and their application in the construction of optimal constant composition codes of weight three":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimal ternary constant composition codes with weight four and distance six")
  Member "improved lower bounds for constant gc content dna codes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dna linear block codes generation error detection and error correction of dna codeword")
  Member "the sizes of optimal q ary codes of weight three and distance four a complete solution":
    also connected through other hyperedges:
      (none)
  Member "constructions for q ary constant weight codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scw codes for maximum likelihood detection in diffusive molecular communications without channel state information")
  Member "the pbd closure of constant composition codes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "importance of symbol equity in coded modulation for power line communications")
  Member "channel coding the road to channel capacity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimal constant weight codes under l_1 metric")

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)