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 "concept and experimental demonstration of optical im dd end to end system optimization using a generative model"):
  Abstract: We perform an experimental end-to-end transceiver optimization via deep learning using a generative adversarial network to approximate the test-bed channel. Previously, optimization was only possible through a prior assumption of an explicit simplified channel model.

Includes members:
  Member "end to end optimized transmission over dispersive intensity modulated channels using bidirectional recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning for communication over dispersive nonlinear channels performance and comparison with classical digital signal processing")
  Member "deep learning for communication over dispersive nonlinear channels performance and comparison with classical digital signal processing":
    also connected through other hyperedges:
      (none)
  Member "end to end deep learning of optical fiber communications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deepjscc f deep joint source channel coding of images with feedback")
  Member "channel agnostic end to end learning based communication systems with conditional gan":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "deep energy autoencoder for noncoherent multicarrier mu simo systems")
  Member "an introduction to deep learning for the physical layer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")

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)