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: "spectrally and energy efficient ofdm see ofdm for intensity modulated optical wireless systems"
  Abstract: Spectrally and energy efficient orthogonal frequency division multiplexing (SEE-OFDM) is an optical OFDM technique based on combining multiple asymmetrically clipped optical OFDM (ACO-OFDM) signals into one OFDM signal. By summing different components together, SEE-OFDM can achieve the same spectral efficiency as DC-biased optical OFDM (DCO-OFDM) without an energy-inefficient DC-bias. This paper introduces multiple methods for decoding a SEE-OFDM symbol and shows that an iterative decoder with hard decisions gives the best performance. Being a multi-component format, different energy allocation amongst the different components of SEE-OFDM is possible. However, equal energy allocation performs 1.5 dB better than unequal energy allocation. A hard-decision, iterative subtraction receiver can further increase performance by another 1.5 dB over soft-decision subtraction and reconstruction receivers. SEE-OFDM consistently performs 3 dB or better and with higher spectral efficiency than ACO-OFDM at the same bit-error-rate (BER). Comparing other combination methods at the same BER, SEE-OFDM performs up to 3 dB better than hybrid asymmetrically clipped optical (OFDM) (HACO-OFDM) and up to 1.5 dB better than asymmetrically and symmetrically clipped optical OFDM (ASCO-OFDM) and enhanced unipolar OFDM (eU-OFDM) when using hard decisions at the receiver. Additionally, SEE-OFDM has the best peak-to-average-power rate (PAPR) as compared to the other combination OFDM formats and ACO-OFDM, which makes it excellent for any range limited optical source, such as laser diodes and light-emitting diodes (LEDs). In summary, SEE-OFDM is shown to have excellent properties to glean additional capacity from an intensity modulation and direct detection (IM/DD) optical wireless communications system.

Connected through hyperedge HE-1 (induced by source paper "a comparative study of unipolar ofdm schemes in gaussian optical intensity channel"):
  with members:
    - "on the capacity of bandlimited optical intensity channels with gaussian noise"
    - "comparisons of spectrally enhanced asymmetrically clipped optical ofdm systems"
    - "evm and achievable data rate analysis of clipped ofdm signals in visible light communication"
    - "a general framework for transmission with transceiver distortion and some applications"
    - "flip ofdm for unipolar communication systems"
    - "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)