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: "an efficient flicker free fec coding scheme for dimmable visible light communication based on polar codes"
  Abstract: Visible light communication (VLC) can provide short-range optical wireless communication, together with illumination using light-emitting diode (LED) lightings. Since conventional forward error correction (FEC) codes cannot provide two lighting related features, dimming support, and flicker mitigation, the existing coding schemes for reliable VLC usually rely on auxiliary coding techniques, which cause a complicated structure and a low transmission efficiency. In this paper, based on polar codes, an efficient and flicker-free FEC coding scheme for dimmable VLC is proposed to increase the transmission efficiency and simplify the coding structure. Taking advantage of polar codes’ recursive encoding structure, the proposed scheme can guarantee the equal probability and the short runs of 1 s and 0 s for arbitrary code rate without extra coding components. Numerical results show that the proposed scheme can have twice higher transmission efficiency than the existing schemes. Furthermore, at a dimming ratio of   $25\%$   or    $75\%$  , the coding gain of the proposed scheme is about 4.6 dB and 1.4 dB higher than that of the Reed–Solomon (RS) codes based scheme and the low-density parity check code based scheme, respectively.

Connected through hyperedge HE-1 (induced by source paper "a deep learning approach to universal binary visible light communication transceiver"):
  with members:
    - "end to end deep learning of optical fiber communications"
    - "deep learning framework for wireless systems applications to optical wireless communications"
    - "model free training of end to end communication systems"
    - "an introduction to deep learning for the physical layer"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"
    - "estimating or propagating gradients through stochastic neurons"

Connected through hyperedge HE-2 (induced by source paper "efficient flicker free fec codes using knuth s balancing algorithm for vlc"):
  with members:
    - "a construction for balancing non binary sequences based on gray code prefixes"

Connected through hyperedge HE-3 (induced by source paper "physical layer security for indoor visible light communications secrecy capacity analysis"):
  with members:
    - "on the capacity of free space optical intensity channels"

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)