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: "complex linear physical layer network coding"
  Abstract: This paper presents the results of a comprehensive investigation of complex linear physical-layer network (PNC) in two-way relay channels (TWRC). A critical question at relay R is as follows: "Given channel gain ratio $\eta = h_A/h_B$, where $h_A$ and $h_B$ are the complex channel gains from nodes A and B to relay R, respectively, what is the optimal coefficients $(\alpha,\beta)$ that minimizes the symbol error rate (SER) of $w_N=\alpha w_A+\beta w_B$ when we attempt to detect $w_N$ in the presence of noise?" Our contributions with respect to this question are as follows: (1) We put forth a general Gaussian-integer formulation for complex linear PNC in which $\alpha,\beta,w_A, w_B$, and $w_N$ are elements of a finite field of Gaussian integers, that is, the field of $\mathbb{Z}[i]/q$ where $q$ is a Gaussian prime. Previous vector formulation, in which $w_A$, $w_B$, and $w_N$ were represented by $2$-dimensional vectors and $\alpha$ and $\beta$ were represented by $2\times 2$ matrices, corresponds to a subcase of our Gaussian-integer formulation where $q$ is real prime only. Extension to Gaussian prime $q$, where $q$ can be complex, gives us a larger set of signal constellations to achieve different rates at different SNR. (2) We show how to divide the complex plane of $\eta$ into different Voronoi regions such that the $\eta$ within each Voronoi region share the same optimal PNC mapping $(\alpha_{opt},\beta_{opt})$. We uncover the structure of the Voronoi regions that allows us to compute a minimum-distance metric that characterizes the SER of $w_N$ under optimal PNC mapping $(\alpha_{opt},\beta_{opt})$. Overall, the contributions in (1) and (2) yield a toolset for a comprehensive understanding of complex linear PNC in $\mathbb{Z}[i]/q$. We believe investigation of linear PNC beyond $\mathbb{Z}[i]/q$ can follow the same approach.

Connected through hyperedge HE-1 (induced by source paper "coherent detection for short packet physical layer network coding with fsk modulation"):
  with members:
    - "noncoherent detection for physical layer network coding"
    - "practical power balanced non orthogonal multiple access"
    - "ultra reliable short packet communications half duplex or full duplex relaying"
    - "mobile lattice coded physical layer network coding with practical channel alignment"
    - "on the subtleties of q pam linear physical layer network coding"
    - "reliable physical layer network coding supporting real applications"
    - "ultra reliable communication in 5g wireless systems"
    - "physical layer network coding tutorial survey and beyond"

Connected through hyperedge HE-2 (induced by source paper "implementation of uplink network coded modulation for two hop networks"):
  with members:
    - "asynchronous convolutional coded physical layer network coding"
    - "network coded multiple access"
    - "wireless network coding via modified 802 11 mac phy design and implementation on sdr"
    - "asynchronous physical layer network coding"
    - "implementation of physical layer network coding"
    - "physical layer network coding"

Connected through hyperedge HE-3 (induced by source paper "physical layer network coding with multiple antennas an enabling technology for smart cities"):
  with members:
    - "an algebraic approach to physical layer network coding"
    - "random access with physical layer network coding"
    - "reliable physical layer network coding"
    - "physical layer network coding tutorial survey and beyond"

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)