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: "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.

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"
    - "on vector linear solvability of multicast networks"
    - "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 "subspace packings"):
  with members:
    - "construction of subspace codes through linkage"
    - "vector network coding based on subspace codes outperforms scalar linear network coding"
    - "construction of large constant dimension codes with a prescribed minimum distance"

Connected through hyperedge HE-3 (induced by source paper "vector network coding based on subspace codes outperforms scalar linear network coding"):
  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"
    - "on the message dimensions of vector linearly solvable networks"
    - "on vector linear solvability of multicast networks"
    - "multicast network coding and field sizes"
    - "efficient erasure decoding of reed solomon codes"
    - "error correcting codes in projective spaces via rank metric codes and ferrers diagrams"
    - "a rank metric approach to error control in random network coding"

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)