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 "vector network coding based on subspace codes outperforms scalar linear network coding"):
  Abstract: This paper considers vector network coding solutions based on rank-metric codes and subspace codes. The main result of this paper is that vector solutions can significantly reduce the required alphabet size compared to the optimal scalar linear solution for the same multicast network. The multicast networks considered in this paper have one source with $h$ messages, and the vector solution is over a field of size $q$ with vectors of length~$t$. For a given network, let the smallest field size for which the network has a scalar linear solution be $q_s$, then the gap in the alphabet size between the vector solution and the scalar linear solution is defined to be $q_s-q^t$. In this contribution, the achieved gap is $q^{(h-2)t^2/h + o(t)}$ for any $q \geq 2$ and any even $h \geq 4$. If $h \geq 5$ is odd, then the achieved gap of the alphabet size is $q^{(h-3)t^2/(h-1) + o(t)}$. Previously, only a gap of size size one had been shown for networks with a very large number of messages. These results imply the same gap of the alphabet size between the optimal scalar linear and some scalar nonlinear network coding solution for multicast networks. For three messages, we also show an advantage of vector network coding, while for two messages the problem remains open. Several networks are considered, all of them are generalizations and modifications of the well-known combination networks. The vector network codes that are used as solutions for those networks are based on subspace codes, particularly subspace codes obtained from rank-metric codes. Some of these codes form a new family of subspace codes, which poses a new research problem.

Includes members:
  Member "grassmannian codes with new distance measures for network coding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the gap between scalar and vector solutions of generalized combination networks")
  Member "linear network coding over rings part ii vector codes and non commutative alphabets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "linear network coding effects of varying the message dimension on the set of characteristics")
  Member "linear network coding over rings part i scalar codes and commutative alphabets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "linear network coding effects of varying the message dimension on the set of characteristics")
  Member "on the message dimensions of vector linearly solvable networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "linear network coding effects of varying the message dimension on the set of characteristics")
  Member "on vector linear solvability of multicast networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the gap between scalar and vector solutions of generalized combination networks")
  Member "multicast network coding and field sizes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "linear network coding effects of varying the message dimension on the set of characteristics")
  Member "efficient erasure decoding of reed solomon codes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hierarchical coded matrix multiplication")
  Member "error correcting codes in projective spaces via rank metric codes and ferrers diagrams":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a note on echelon ferrers construction")

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)