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: "frequency shift filtering for ofdm signal recovery in narrowband power line communications"
  Abstract: Power line communications (PLC) has been drawing considerable interest in recent years due to the growing interest in smart grid implementation. Specifically, network control and grid applications are allocated the frequency band of 0-500 kHz, commonly referred to as the narrowband PLC channel. This frequency band is characterized by strong periodic noise which results in low signal to noise ratio (SNR). In this work we propose a receiver which uses frequency shift filtering to exploit the cyclostationary properties of both the narrowband power line noise, as well as the information signal, digitally modulated using orthogonal frequency division multiplexing. An adaptive implementation for the proposed receiver is presented as well. The proposed receiver is compared to existing receivers via analysis and simulation. The results show that the receiver proposed in this work obtains a substantial performance gain over previously proposed receivers, without requiring any coordination with the transmitter.

Connected through hyperedge HE-1 (induced by source paper "the rate distortion function of asynchronously sampled memoryless cyclostationary gaussian processes"):
  with members:
    - "the capacity of memoryless channels with sampled cyclostationary gaussian noise"
    - "on the capacity of narrowband plc channels"
    - "the distortion rate function of cyclostationary gaussian processes"
    - "on the optimal compressions in the compress and forward relay schemes"
    - "the generalized asymptotic equipartition property necessary and sufficient conditions"
    - "achieving the gaussian rate distortion function by prediction"

Connected through hyperedge HE-2 (induced by source paper "viterbinet a deep learning based viterbi algorithm for symbol detection"):
  with members:
    - "machine learning in the air"
    - "deep unfolded robust pca with application to clutter suppression in ultrasound"
    - "deep learning methods for improved decoding of linear codes"
    - "a very brief introduction to machine learning with applications to communication systems"
    - "an introduction to deep learning for the physical layer"
    - "on deep learning based channel decoding"
    - "conditional image generation with pixelcnn decoders"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-3 (induced by source paper "the capacity of memoryless channels with sampled cyclostationary gaussian noise"):
  with members:
    - "analog to digital cognitive radio sampling detection and hardware"
    - "on the capacity of mimo broadband power line communications channels"
    - "sub nyquist cyclostationary detection for cognitive radio"
    - "on the capacity of narrowband plc channels"
    - "channel capacity under sub nyquist nonuniform sampling"
    - "what will 5g be"
    - "shannon meets nyquist capacity of sampled gaussian channels"
    - "on the minimax capacity loss under sub nyquist universal sampling"

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.OH(Other Computer Science)