human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "model free training of end to end communication systems"):
  Abstract: The idea of end-to-end learning of communication systems through neural network-based autoencoders has the shortcoming that it requires a differentiable channel model. We present in this paper a novel learning algorithm which alleviates this problem. The algorithm enables training of communication systems with an unknown channel model or with non-differentiable components. It iterates between training of the receiver using the true gradient, and training of the transmitter using an approximation of the gradient. We show that this approach works as well as model-based training for a variety of channels and tasks. Moreover, we demonstrate the algorithm's practical viability through hardware implementation on software-defined radios where it achieves state-of-the-art performance over a coaxial cable and wireless channel.

Includes members:
  Member "one bit ofdm receivers via deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "golay layer limiting peak to average power ratio for ofdm based autoencoders")
  Member "achievable information rates for nonlinear fiber communication via end to end autoencoder learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "end to end optimized transmission over dispersive intensity modulated channels using bidirectional recurrent neural networks")
  Member "deep joint source channel coding for wireless image transmission":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep joint transmission recognition for power constrained iot devices")
  Member "neural joint source channel coding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distortion agnostic deep watermarking")
  Member "ofdm autoencoder for end to end learning of communications systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "golay layer limiting peak to average power ratio for ofdm based autoencoders")
  Member "channel agnostic end to end learning based communication systems with conditional gan":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "wgan based autoencoder training over the air")
  Member "end to end learning of communications systems without a channel model":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep energy autoencoder for noncoherent multicarrier mu simo systems")
  Member "approximating the void learning stochastic channel models from observation with variational generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep learning based end to end wireless communication systems with conditional gan as unknown channel")

Question: Please tell me which class the source paper 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)