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: "one bit ofdm receivers via deep learning"
  Abstract: This paper develops novel deep learning-based architectures and design methodologies for an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one-bit complex quantization. Single bit quantization greatly reduces complexity and power consumption, but makes accurate channel estimation and data detection difficult. This is particularly true for multicarrier waveforms, which have high peak-to-average ratio in the time domain and fragile subcarrier orthogonality in the frequency domain. The severe distortion for one-bit quantization typically results in an error floor even at moderately low signal-to-noise-ratio (SNR) such as 5 dB. For channel estimation (using pilots), we design a novel generative supervised deep neural network (DNN) that can be trained with a reasonable number of pilots. After channel estimation, a neural network-based receiver -- specifically, an autoencoder -- jointly learns a precoder and decoder for data symbol detection. Since quantization prevents end-to-end training, we propose a two-step sequential training policy for this model. With synthetic data, our deep learning-based channel estimation can outperform least squares (LS) channel estimation for unquantized (full-resolution) OFDM at average SNRs up to 14 dB. For data detection, our proposed design achieves lower bit error rate (BER) in fading than unquantized OFDM at average SNRs up to 10 dB.

Connected through hyperedge HE-1 (induced by source paper "golay layer limiting peak to average power ratio for ofdm based autoencoders"):
  with members:
    - "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"
    - "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 "high rate communication over one bit quantized channels via deep learning and ldpc codes"):
  with members:
    - "autoencoder based error correction coding for one bit quantization"
    - "learning a code machine learning for approximate non linear coded computation"
    - "on deep learning based channel decoding"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-3 (induced by source paper "an adaptive deep learning algorithm based autoencoder for interference channels"):
  with members:
    - "physical adversarial attacks against end to end autoencoder communication systems"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "deep reinforcement learning autoencoder with noisy feedback"
    - "end to end learning of communications systems without a channel model"
    - "deep learning for joint source channel coding of text"
    - "an introduction to deep learning for the physical layer"
    - "training of deep neural networks based on distance measures using rmsprop"
    - "deep learning based mimo communications"

Connected through hyperedge HE-4 (induced by source paper "a regression approach to certain information transmission problems"):
  with members:
    - "learning to detect"
    - "mmwave beam prediction with situational awareness a machine learning approach"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "deepcode feedback codes via deep learning"
    - "learning the mmse channel estimator"
    - "a model driven deep learning network for mimo detection"
    - "a two step learning and interpolation method for location based channel database"
    - "mixed adc massive mimo"

Connected through hyperedge HE-5 (induced by source paper "artificial intelligence aided receiver for a cp free ofdm system design simulation and experimental test"):
  with members:
    - "deep learning in physical layer communications"
    - "probabilistic mimo symbol detection with expectation consistency approximate inference"
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "model driven deep learning for physical layer communications"
    - "a model driven deep learning network for mimo detection"
    - "deep learning based csi feedback approach for time varying massive mimo channels"
    - "an introduction to deep learning for the physical layer"
    - "rapro a novel 5g rapid prototyping system architecture"

Connected through hyperedge HE-6 (induced by source paper "autoencoder based error correction coding for one bit quantization"):
  with members:
    - "learning a code machine learning for approximate non linear coded computation"
    - "deepcode feedback codes via deep learning"
    - "deep learning methods for improved decoding of linear codes"
    - "an introduction to deep learning for the physical layer"
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "on deep learning based channel decoding"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-7 (induced by source paper "design of communication systems using deep learning a variational inference perspective"):
  with members:
    - "learning to detect"
    - "constrained cnn losses for weakly supervised segmentation"
    - "end to end deep learning of optical fiber communications"
    - "deep learning in physical layer communications"
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "model free training of end to end communication systems"
    - "an introduction to deep learning for the physical layer"
    - "imposing hard constraints on deep networks promises and limitations"

Connected through hyperedge HE-8 (induced by source paper "supervised learning for multi hop mu mimo communications with one bit transceivers"):
  with members:
    - "ofdm autoencoder for end to end learning of communications systems"
    - "multipair massive mimo relaying systems with one bit adc s and dac s"
    - "one bit sphere decoding for uplink massive mimo systems with one bit adcs"
    - "an introduction to deep learning for the physical layer"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "detection algorithms for communication systems using deep learning"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "quantized massive mu mimo ofdm uplink"

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)