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: "the capacity of random linear coding networks as subspace channels"
  Abstract: In this paper, we consider noncoherent random linear coding networks (RLCNs) as a discrete memoryless channel (DMC) whose input and output alphabets consist of subspaces. This contrasts with previous channel models in the literature which assume matrices as the channel input and output. No particular assumptions are made on the network topology or the transfer matrix, except that the latter may be rank-deficient according to some rank deficiency probability distribution. We introduce a random vector basis selection procedure which renders the DMC symmetric. The capacity we derive can be seen as a lower bound on the capacity of noncoherent RLCNs, where subspace coding suffices to achieve this bound.

Connected through hyperedge HE-1 (induced by source paper "on the capacity of multiplicative finite field matrix channels"):
  with members:
    - "beyond the cut set bound uncertainty computations in network coding with correlated sources"
    - "beyond the cut set bound uncertainty computations in network coding with correlated sources"
    - "coding for linear operator channels over finite fields"
    - "communication over finite field matrix channels"
    - "coding for errors and erasures in random network coding"
    - "on the role of mmse estimation in approaching the information theoretic limits of linear gaussian channels shannon meets wiener"

Connected through hyperedge HE-2 (induced by source paper "an upper bound on broadcast subspace codes"):
  with members:
    - "on the capacity of non coherent network coding"
    - "coding for linear operator channels over finite fields"
    - "multishot codes for network coding using rank metric codes"
    - "communication over finite field matrix channels"
    - "towards the capacity region of multiplicative linear operator broadcast channels"
    - "multishot codes for network coding bounds and a multilevel construction"
    - "a rank metric approach to error control in random network coding"
    - "coding for errors and erasures in random network coding"

Connected through hyperedge HE-3 (induced by source paper "towards the capacity region of multiplicative linear operator broadcast channels"):
  with members:
    - "on the capacity of non coherent network coding"
    - "recursive code construction for random networks"
    - "coding for linear operator channels over finite fields"
    - "communication over finite field matrix channels"
    - "multishot codes for network coding bounds and a multilevel construction"
    - "projective space codes for the injection metric"
    - "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)