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: "suppressing alignment joint papr and out of band power leakage reduction for ofdm based systems"
  Abstract: Orthogonal frequency division multiplexing (OFDM) inherently suffers from two major drawbacks: high out-of-band (OOB) power leakage and high peak-to-average power ratio (PAPR). This paper proposes a novel approach called suppressing alignment for the joint reduction of the OOB power leakage and PAPR. The proposed approach exploits the temporal degrees of freedom provided by the cyclic prefix (CP), a necessary redundancy in OFDM systems, to generate a suppressing signal, that when added to the OFDM symbol, results in marked reduction in both the OOB power leakage and PAPR. Additionally, and in order to not cause any interference to the information data carried by the OFDM symbol, the proposed approach utilizes the wireless channel to perfectly align the suppressing signal with the CP duration at the OFDM receiver. Essentially, maintaining a bit error rate (BER) performance similar to legacy OFDM without requiring any change in the receiver structure.

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"
    - "a generic complementary sequence encoder"
    - "an introduction to deep learning for the physical layer"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "learning to communicate channel auto encoders domain specific regularizers and attention"

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)