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: "a generic complementary sequence encoder"
  Abstract: In this study, we propose a generic complementary sequence (CS) encoder that limits the peak-to-average-power ratio (PAPR) of an orthogonal frequency division multiplexing (OFDM) symbol for a non-contiguous frequency domain resource allocation. To this end, we introduce a framework which describes a recursion evolved with two linear operators at each step as an encoding operation. The framework algebraically determines how the operators applied at each recursion step are distributed to the coefficients of a polynomial via binary construction sequences. By applying the introduced framework to a recursive Golay complementary pair (GCP) construction relying on Budi$s$in's methods, we show the impact of the initial sequences, phase rotations, signs, real scalars, and the shifting factors applied at each step on the elements of the sequences in a GCP explicitly. Hence, we provide further insights into GCPs. As a result, we obtain an encoder which generates non-contiguous CSs, i.e., CSs with zero symbols, by deriving the algebraic normal forms (ANFs) for the shifting factors. Thus, three important aspects for communications, i.e., frequency diversity, coding gain, and low PAPR, are achieved simultaneously for OFDM symbols. As another result, we extend the standard sequences by separating the encoders that control the amplitude and the phase of the elements of a CSs. We also demonstrate that the proposed encoder generates CSs with quadrature amplitude modulation (QAM) alphabet.

Connected through hyperedge HE-1 (induced by source paper "golay layer limiting peak to average power ratio for ofdm based autoencoders"):
  with members:
    - "one bit ofdm receivers via deep learning"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "model free training of end to end communication systems"
    - "an introduction to deep learning for the physical layer"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "suppressing alignment joint papr and out of band power leakage reduction for ofdm based systems"
    - "learning to communicate channel auto encoders domain specific regularizers and attention"

Connected through hyperedge HE-2 (induced by source paper "an uplink control channel design with complementary sequences for unlicensed bands"):
  with members:
    - "a reliable uplink control channel design with complementary sequences"
    - "a comparison of sc fde and uw dft s ofdm for millimeter wave communications"
    - "simple maximum likelihood decoding of generalized first order reed muller codes"

Connected through hyperedge HE-3 (induced by source paper "a reliable uplink control channel design with complementary sequences"):
  with members:
    - "a comparison of sc fde and uw dft s ofdm for millimeter wave communications"

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)