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: "channel agnostic end to end learning based communication systems with conditional gan"
  Abstract: In this article, we use deep neural networks (DNNs) to develop a wireless end-to-end communication system, in which DNNs are employed for all signal-related functionalities, such as encoding, decoding, modulation, and equalization. However, accurate instantaneous channel transfer function, \emph{i.e.}, the channel state information (CSI), is necessary to compute the gradient of the DNN representing. In many communication systems, the channel transfer function is hard to obtain in advance and varies with time and location. In this article, this constraint is released by developing a channel agnostic end-to-end system that does not rely on any prior information about the channel. We use a conditional generative adversarial net (GAN) to represent the channel effects, where the encoded signal of the transmitter will serve as the conditioning information. In addition, in order to deal with the time-varying channel, the received signal corresponding to the pilot data can also be added as a part of the conditioning information. From the simulation results, the proposed method is effective on additive white Gaussian noise (AWGN) and Rayleigh fading channels, which opens a new door for building data-driven communication systems.

Connected through hyperedge HE-1 (induced by source paper "wgan based autoencoder training over the air"):
  with members:
    - "concept and experimental demonstration of optical im dd end to end system optimization using a generative model"
    - "trainable communication systems concepts and prototype"
    - "model free training of end to end communication systems"
    - "an introduction to deep learning for the physical layer"
    - "learning to communicate channel auto encoders domain specific regularizers and attention"
    - "deep residual learning for image recognition"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-2 (induced by source paper "deep energy autoencoder for noncoherent multicarrier mu simo systems"):
  with members:
    - "end to end learning of communications systems without a channel model"
    - "an introduction to deep learning for the physical layer"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "massive machine type communications in 5g physical and mac layer solutions"
    - "deep learning in neural networks"
    - "energy based modulation for noncoherent massive simo systems"
    - "performance analysis of energy detection based massive simo"

Connected through hyperedge HE-3 (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-4 (induced by source paper "experienced deep reinforcement learning with generative adversarial networks gans for model free ultra reliable low latency communication"):
  with members:
    - "generative adversarial networks for distributed intrusion detection in the internet of things"
    - "deep learning based end to end wireless communication systems with conditional gan as unknown channel"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "joint user association and resource allocation optimization for ultra reliable low latency hetnets"
    - "federated learning for ultra reliable low latency v2v communications"
    - "robust deep reinforcement learning for security and safety in autonomous vehicle systems"
    - "risk aware resource allocation for urllc challenges and strategies with machine learning"
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"

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"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "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 "unsupervised linear and nonlinear channel equalization and decoding using variational autoencoders"):
  with members:
    - "on the convergence of adam and beyond"
    - "learning from the syndrome"
    - "deep learning methods for improved decoding of linear codes"
    - "an introduction to deep learning for the physical layer"
    - "deep complex networks"
    - "scaling deep learning based decoding of polar codes via partitioning"
    - "on deep learning based channel decoding"
    - "learning to decode linear codes using deep learning"

Connected through hyperedge HE-7 (induced by source paper "artificial intelligence aided receiver for a cp free ofdm system design simulation and experimental test"):
  with members:
    - "one bit ofdm receivers via deep learning"
    - "deep learning in physical layer communications"
    - "probabilistic mimo symbol detection with expectation consistency approximate inference"
    - "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-8 (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"
    - "one bit ofdm receivers via deep learning"
    - "end to end deep learning of optical fiber communications"
    - "deep learning in physical layer communications"
    - "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"

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)