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 approximating gaussian relay networks by deterministic networks"
  Abstract: We examine the extent to which Gaussian relay networks can be approximated by deterministic networks, and present two results, one negative and one positive. #R##N#The gap between the capacities of a Gaussian relay network and a corresponding linear deterministic network can be unbounded. The key reasons are that the linear deterministic model fails to capture the phase of received signals, and there is a loss in signal strength in the reduction to a linear deterministic network. #R##N#On the positive side, Gaussian relay networks are indeed well approximated by certain discrete superposition networks, where the inputs and outputs to the channels are discrete, and channel gains are signed integers. #R##N#As a corollary, MIMO channels cannot be approximated by the linear deterministic model but can be by the discrete superposition model.

Connected through hyperedge HE-1 (induced by source paper "a digital interface for gaussian relay and interference networks lifting codes from the discrete superposition model"):
  with members:
    - "wireless network information flow a deterministic approach"
    - "a digital interface for gaussian relay networks lifting codes from the discrete superposition model to gaussian relay networks"
    - "noisy network coding"
    - "fading broadcast channels with state information at the receivers"
    - "a new outer bound and the noisy interference sum rate capacity for gaussian interference channels"
    - "capacity bounds for the gaussian interference channel"
    - "approximate capacity of the two way relay channel a deterministic approach"
    - "the two user gaussian interference channel a deterministic view"

Connected through hyperedge HE-2 (induced by source paper "approximately optimal wireless broadcasting"):
  with members:
    - "wireless network information flow a deterministic approach"
    - "a digital interface for gaussian relay and interference networks lifting codes from the discrete superposition model"
    - "algebraic network coding approach to deterministic wireless relay networks"
    - "noisy network coding"
    - "linear finite field deterministic networks with many sources and one destination"
    - "reciprocity in linear deterministic networks under linear coding"
    - "a combinatorial study of linear deterministic relay networks"
    - "cooperative relay broadcast channels"

Connected through hyperedge HE-3 (induced by source paper "a digital interface for gaussian relay networks lifting codes from the discrete superposition model to gaussian relay networks"):
  with members:
    - "wireless network information flow a deterministic approach"
    - "the two user gaussian interference channel a deterministic view"
    - "a deterministic approach to wireless relay 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)