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 "grassmannian codes with new distance measures for network coding"):
  Abstract: Subspace codes are known to be useful in error-correction for random network coding. Recently, they were used to prove that vector network codes outperform scalar linear network codes, on multicast networks, with respect to the alphabet size. In both cases, the subspace distance is used as the distance measure. In this work we show that we can replace the subspace distance with two other possible distance measures which generalize the subspace distance. We prove that each code with the largest number of codewords and the generalized distance, given the other parameters, has the minimum requirements needed to solve a given multicast network with a scalar linear code. We discuss lower and upper bounds on the sizes of the related codes.

Includes members:
  Member "vector network coding based on subspace codes outperforms scalar linear network coding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "roos bound for skew cyclic codes in hamming and rank metric")
  Member "subspace codes based on graph matchings ferrers diagrams and pending blocks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distance distributions of cyclic orbit codes")
  Member "the asymptotic behavior of grassmannian codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "problems on q analogs in coding theory")
  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 "error correcting codes in projective spaces via rank metric codes and ferrers diagrams":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a note on echelon ferrers construction")
  Member "a rank metric approach to error control in random network coding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "on the gap between scalar and vector solutions of generalized combination networks")

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)