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: "ofdm autoencoder for end to end learning of communications systems"
  Abstract: We extend the idea of end-to-end learning of communications systems through deep neural network (NN)-based autoencoders to orthogonal frequency division multiplexing (OFDM) with cyclic prefix (CP). Our implementation has the same benefits as a conventional OFDM system, namely singletap equalization and robustness against sampling synchronization errors, which turned out to be one of the major challenges in previous single-carrier implementations. This enables reliable communication over multipath channels and makes the communication scheme suitable for commodity hardware with imprecise oscillators. We show that the proposed scheme can be realized with state-of-the-art deep learning software libraries as transmitter and receiver solely consist of differentiable layers required for gradient-based training. We compare the performance of the autoencoder-based system against that of a state-of-the-art OFDM baseline over frequency-selective fading channels. Finally, the impact of a non-linear amplifier is investigated and we show that the autoencoder inherently learns how to deal with such hardware impairments.

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"
    - "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 "machine learning in the air"):
  with members:
    - "successive refinement of images with deep joint source channel coding"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "deep learning for distributed optimization applications to wireless resource management"
    - "federated learning over wireless fading channels"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "machine learning for wireless communications in the internet of things a comprehensive survey"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "toward intelligent vehicular networks a machine learning framework"

Connected through hyperedge HE-3 (induced by source paper "learning to communicate and energize modulation coding and multiple access designs for wireless information power transmission"):
  with members:
    - "learning modulation design for swipt with nonlinear energy harvester large and small signal power regimes"
    - "signal and system design for wireless power transfer prototype experiment and validation"
    - "fundamentals of wireless information and power transfer from rf energy harvester models to signal and system designs"
    - "deep learning methods for improved decoding of linear codes"
    - "model free training of end to end communication systems"
    - "a learning approach to wireless information and power transfer signal and system design"
    - "an introduction to deep learning for the physical layer"
    - "on the capacity of swipt systems with a nonlinear energy harvesting circuit"

Connected through hyperedge HE-4 (induced by source paper "turbo autoencoder deep learning based channel codes for point to point communication channels"):
  with members:
    - "deepcode feedback codes via deep learning"
    - "deep learning methods for improved decoding of linear codes"
    - "neural joint source channel coding"
    - "end to end learning of communications systems without a channel model"
    - "an introduction to deep learning for the physical layer"
    - "don t decay the learning rate increase the batch size"
    - "scaling deep learning based decoding of polar codes via partitioning"
    - "comparative study of cnn and rnn for natural language processing"

Connected through hyperedge HE-5 (induced by source paper "deep learning based end to end wireless communication systems with conditional gan as unknown channel"):
  with members:
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "deep learning in physical layer communications"
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "large scale gan training for high fidelity natural image synthesis"
    - "end to end learning of communications systems without a channel model"
    - "a model driven deep learning network for mimo detection"
    - "approximating the void learning stochastic channel models from observation with variational generative adversarial networks"
    - "deep learning for joint source channel coding of text"

Connected through hyperedge HE-6 (induced by source paper "learning modulation design for swipt with nonlinear energy harvester large and small signal power regimes"):
  with members:
    - "fundamentals of wireless information and power transfer from rf energy harvester models to signal and system designs"
    - "deep learning methods for improved decoding of linear codes"
    - "model free training of end to end communication systems"
    - "a learning approach to wireless information and power transfer signal and system design"
    - "an introduction to deep learning for the physical layer"
    - "on the capacity of swipt systems with a nonlinear energy harvesting circuit"
    - "on capacity achieving distributions over complex awgn channels under nonlinear power constraints and their applications to swipt"
    - "wireless information and power transfer over an awgn channel nonlinearity and asymmetric gaussian signaling"

Connected through hyperedge HE-7 (induced by source paper "supervised learning for multi hop mu mimo communications with one bit transceivers"):
  with members:
    - "one bit ofdm receivers via deep learning"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "when machine learning meets wireless cellular networks deployment challenges and applications"):
  with members:
    - "deepcabac context adaptive binary arithmetic coding for deep neural network compression"
    - "human centered artificial intelligence and machine learning"
    - "machine learning in the air"
    - "scalable agent alignment via reward modeling a research direction"
    - "rogue drone detection a machine learning approach"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "cell free massive mimo versus small cells"

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)