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: "evm and achievable data rate analysis of clipped ofdm signals in visible light communication"
  Abstract: Orthogonal frequency division multiplexing (OFDM) has been considered for visible light communication (VLC); thanks to its ability to boost data rates as well as its robustness against frequency-selective fading channels. A major disadvantage of OFDM is the large dynamic range of its time-domain waveforms, making OFDM vulnerable to nonlinearity of light emitting diodes. DC-biased optical OFDM (DCO-OFDM) and asymmetrically clipped optical OFDM (ACO-OFDM) are two popular OFDM techniques developed for the VLC. In this article, we will analyze the performance of the DCO-OFDM and ACO-OFDM signals in terms of error vector magnitude (EVM), signal-to-distortion ratio (SDR), and achievable data rates under both average optical power and dynamic optical power constraints. EVM is a commonly used metric to characterize distortions. We will describe an approach to numerically calculate the EVM for DCO-OFDM and ACO-OFDM. We will derive the optimum biasing ratio in the sense of minimizing EVM for DCO-OFDM. In addition, we will formulate the EVM minimization problem as a convex linear optimization problem and obtain an EVM lower bound against which to compare the DCO-OFDM and ACO-OFDM techniques. We will prove that the ACO-OFDM can achieve the lower bound. Average optical power and dynamic optical power are two main constraints in VLC. We will derive the achievable data rates under these two constraints for both additive white Gaussian noise channel and frequency-selective channel. We will compare the performance of DCO-OFDM and ACO-OFDM under different power constraint scenarios.

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"
    - "spectrally and energy efficient ofdm see ofdm for intensity modulated optical wireless systems"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "using delta sigma modulators in visible light ofdm systems"):
  with members:
    - "brightness control in dynamic range constrained visible light ofdm systems"
    - "distributions of upper papr and lower papr of ofdm signals in visible light communications"
    - "quantization noise shaping for information maximizing adcs"

Connected through hyperedge HE-3 (induced by source paper "brightness control in dynamic range constrained visible light ofdm systems"):
  with members:
    - "distributions of upper papr and lower papr of ofdm signals in visible light communications"
    - "using delta sigma modulators in visible light ofdm systems"

Connected through hyperedge HE-4 (induced by source paper "distributions of upper papr and lower papr of ofdm signals in visible light communications"):
  with members:
    - "brightness control in dynamic range constrained visible light ofdm systems"
    - "can visible light communications provide gb s service"

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)