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 "geometric constellation shaping for fiber optic communication systems via end to end learning"):
  Abstract: In this paper, an unsupervised machine learning method for geometric constellation shaping is investigated. By embedding a differentiable fiber channel model within two neural networks, the learning algorithm is optimizing for a geometric constellation shape. The learned constellations yield improved performance to state-of-the-art geometrically shaped constellations, and include an implicit trade-off between amplification noise and nonlinear effects. Further, the method allows joint optimization of system parameters, such as the optimal launch power, simultaneously with the constellation shape. An experimental demonstration validates the findings. Improved performances are reported, up to 0.13 bit/4D in simulation and experimentally up to 0.12 bit/4D.

Includes members:
  Member "experimental demonstration of geometrically shaped constellations tailored to the nonlinear fibre channel":
    also connected through other hyperedges:
      (none)
  Member "a survey on application of machine learning techniques in optical networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a very brief introduction to machine learning with applications to communication systems")
  Member "deep learning of the nonlinear schr odinger equation in fiber optic communications":
    also connected through other hyperedges:
      (none)
  Member "end to end learning of communications systems without a channel model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep energy autoencoder for noncoherent multicarrier mu simo systems")
  Member "deep learning of geometric constellation shaping including fiber nonlinearities":
    also connected through other hyperedges:
      (none)
  Member "an introduction to deep learning for the physical layer":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "applying neural networks in optical communication systems possible pitfalls":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "end to end optimized transmission over dispersive intensity modulated channels using bidirectional recurrent neural networks")
  Member "impact of 4d channel distribution on the achievable rates in coherent optical communication experiments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "nonlinearity mitigation in wdm systems models strategies and achievable rates")

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)