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: "quasi linear network coding"
  Abstract: We present a heuristic for designing vector non-linear network codes for non-multicast networks, which we call quasi-linear network codes. The method presented has two phases: finding an approximate linear network code over the reals, and then quantizing it to a vector non-linear network code using a fixed-point representation. Apart from describing the method, we draw some links between some network parameters and the rate of the resulting code.

Connected through hyperedge HE-1 (induced by source paper "subspace aware index codes"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "a sparse and low rank optimization framework for index coding via riemannian optimization"
    - "index coding and network coding via rank minimization"
    - "index coding with coded side information"
    - "caching and coded multicasting multiple groupcast index coding"
    - "rank minimization over finite fields fundamental limits and coding theoretic interpretations"
    - "an equivalence between network coding and index coding"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-2 (induced by source paper "index coding and network coding via rank minimization"):
  with members:
    - "on a duality between recoverable distributed storage and index coding"
    - "a matrix completion approach to linear index coding problem"
    - "topological interference management through index coding"
    - "bounding multiple unicasts through index coding and locally repairable codes"
    - "on the capacity region for index coding"
    - "index coding capacity how far can one go with only shannon inequalities"
    - "understanding alternating minimization for matrix completion"
    - "decentralized coded caching attains order optimal memory rate tradeoff"

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)