human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "on vector linear solvability of multicast networks"
  Abstract: Vector linear network coding (LNC) is a generalization of the conventional scalar LNC, such that the data unit transmitted on every edge is an L-dimensional vector of data symbols over a base field GF(q). Vector LNC enriches the choices of coding operations at intermediate nodes, and there is a popular conjecture on the benefit of vector LNC over scalar LNC in terms of alphabet size of data units: there exist (singlesource) multicast networks that are vector linearly solvable of dimension L over GF(q) but not scalar linearly solvable over any field of size q' qL. This paper introduces a systematic way to construct such multicast networks, and subsequently establish explicit instances to affirm the positive answer of this conjecture for infinitely many alphabet sizes pL with respect to an arbitrary prime p. On the other hand, this paper also presents explicit instances with the special property that they do not have a vector linear solution of dimension L over GF(2) but have scalar linear solutions over GF(q') for someq' <; 2#N# L#N#, where q' can be odd or even. This discovery also unveils that over a given base field, a multicast network that has a vector linear solution of dimension L does not necessarily have a vector linear solution of dimension L' > L.

Connected through hyperedge HE-1 (induced by source paper "on the gap between scalar and vector solutions of generalized combination networks"):
  with members:
    - "network coding solutions for the combination network and its subgraphs"
    - "grassmannian codes with new distance measures for network coding"
    - "vector network coding based on subspace codes outperforms scalar linear network coding"
    - "a rank metric approach to error control in random network coding"
    - "coding for errors and erasures in random network coding"

Connected through hyperedge HE-2 (induced by source paper "linear network coding effects of varying the message dimension on the set of characteristics"):
  with members:
    - "linear network coding over rings part ii vector codes and non commutative alphabets"
    - "linear network coding over rings part i scalar codes and commutative alphabets"
    - "capacity and achievable rate regions for linear network coding over ring alphabets"
    - "on the message dimensions of vector linearly solvable networks"
    - "on base field of linear network coding"
    - "vector network coding based on subspace codes outperforms scalar linear network coding"
    - "multicast network coding and field sizes"
    - "linear network coding linear index coding and representable discrete polymatroids"

Connected through hyperedge HE-3 (induced by source paper "delay complexity trade off of random linear network coding in wireless broadcast"):
  with members:
    - "circular shift linear network coding"
    - "circular shift linear network codes with arbitrary odd block lengths"
    - "approximating throughput and packet decoding delay in linear network coded wireless broadcast"
    - "decoding delay performance of random linear network coding for broadcast"
    - "optimal control for network coding broadcast"
    - "decoding and file transfer delay balancing in network coding broadcast"
    - "completion delay minimization for instantly decodable network codes"
    - "throughput delay analysis of random linear network coding for wireless broadcasting"

Connected through hyperedge HE-4 (induced by source paper "circular shift linear network codes with arbitrary odd block lengths"):
  with members:
    - "circular shift linear network coding"
    - "linear network coding over rings part ii vector codes and non commutative alphabets"
    - "a class of non linearly solvable networks"
    - "on the message dimensions of vector linearly solvable networks"
    - "vector network coding based on subspace codes outperforms scalar linear network coding"

Connected through hyperedge HE-5 (induced by source paper "circular shift linear network coding"):
  with members:
    - "linear network coding over rings part ii vector codes and non commutative alphabets"
    - "a class of non linearly solvable networks"
    - "vector network coding based on subspace codes outperforms scalar linear network coding"
    - "multicast network coding and field sizes"

Connected through hyperedge HE-6 (induced by source paper "network coding solutions for minimal combination networks and their sub networks"):
  with members:
    - "vector network coding based on subspace codes outperforms scalar linear network coding"

Connected through hyperedge HE-7 (induced by source paper "linear network coding over rings part i scalar codes and commutative alphabets"):
  with members:
    - "linear network coding over rings part ii vector codes and non commutative alphabets"
    - "a class of non linearly solvable networks"
    - "on base field of linear network coding"
    - "vector network coding based on subspace codes outperforms scalar linear network coding"
    - "multicast network coding and field sizes"
    - "an equivalence between network coding and index coding"

Connected through hyperedge HE-8 (induced by source paper "linear network coding over rings part ii vector codes and non commutative alphabets"):
  with members:
    - "linear network coding over rings part i scalar codes and commutative alphabets"
    - "on the message dimensions of vector linearly solvable networks"

Question: Please tell me which class the center node 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)