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: "end to end deep learning of optical fiber communications"
  Abstract: In this paper, we implement an optical fiber communication system as an end-to-end deep neural network, including the complete chain of transmitter, channel model, and receiver. This approach enables the optimization of the transceiver in a single end-to-end process. We illustrate the benefits of this method by applying it to intensity modulation/direct detection (IM/DD) systems and show that we can achieve bit error rates below the 6.7% hard-decision forward error correction (HD-FEC) threshold. We model all componentry of the transmitter and receiver, as well as the fiber channel, and apply deep learning to find transmitter and receiver configurations minimizing the symbol error rate. We propose and verify in simulations a training method that yields robust and flexible transceivers that allow—without reconfiguration—reliable transmission over a large range of link dispersions. The results from end-to-end deep learning are successfully verified for the first time in an experiment. In particular, we achieve information rates of 42 Gb/s below the HD-FEC threshold at distances beyond 40 km. We find that our results outperform conventional IM/DD solutions based on two- and four-level pulse amplitude modulation with feedforward equalization at the receiver. Our study is the first step toward end-to-end deep learning based optimization of optical fiber communication systems.

Connected through hyperedge HE-1 (induced by source paper "deepjscc f deep joint source channel coding of images with feedback"):
  with members:
    - "successive refinement of images with deep joint source channel coding"
    - "deep learning based polar code design"
    - "joint autoregressive and hierarchical priors for learned image compression"
    - "deepcode feedback codes via deep learning"
    - "deep joint source channel coding for wireless image transmission"
    - "deep learning methods for improved decoding of linear codes"
    - "deep learning for joint source channel coding of text"
    - "an introduction to deep learning for the physical layer"

Connected through hyperedge HE-2 (induced by source paper "deep learning for distributed optimization applications to wireless resource management"):
  with members:
    - "learning optimal resource allocations in wireless systems"
    - "machine learning in the air"
    - "learning to optimize training deep neural networks for interference management"
    - "deep learning framework for wireless systems applications to optical wireless communications"
    - "an introduction to deep learning for the physical layer"
    - "optnet differentiable optimization as a layer in neural networks"
    - "the expressive power of neural networks a view from the width"
    - "proactive eavesdropping via cognitive jamming in fading channels"

Connected through hyperedge HE-3 (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"
    - "deep learning in physical layer communications"
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "trainable communication systems concepts and prototype"):
  with members:
    - "deep learning for the degraded broadcast channel"
    - "joint learning of geometric and probabilistic constellation shaping"
    - "online label recovery for deep learning based communication through error correcting codes"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "model free training of end to end communication systems"
    - "an introduction to deep learning for the physical layer"
    - "learning to decode linear codes using deep learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "does deep learning always outperform simple linear regression in optical imaging"):
  with members:
    - "review on optical image hiding and watermarking techniques"
    - "experimental comparison of single pixel imaging algorithms"
    - "deep learning approach for fourier ptychography microscopy"
    - "computational ghost imaging using deep learning"
    - "phase recovery and holographic image reconstruction using deep learning in neural networks"
    - "solving fourier ptychographic imaging problems via neural network modeling and tensorflow"
    - "lensless computational imaging through deep learning"
    - "deep learning microscopy"

Connected through hyperedge HE-6 (induced by source paper "a deep learning approach to universal binary visible light communication transceiver"):
  with members:
    - "deep learning framework for wireless systems applications to optical wireless communications"
    - "model free training of end to end communication systems"
    - "an introduction to deep learning for the physical layer"
    - "an efficient flicker free fec coding scheme for dimmable visible light communication based on polar codes"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"
    - "estimating or propagating gradients through stochastic neurons"

Connected through hyperedge HE-7 (induced by source paper "deep learning based constellation optimization for physical network coding in two way relay networks"):
  with members:
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "end to end learning of communications systems without a channel model"
    - "deep learning for joint source channel coding of text"
    - "an introduction to deep learning for the physical layer"
    - "adam a method for stochastic optimization"
    - "physical layer network coding tutorial survey and beyond"
    - "channel coding and decoding in a relay system operated with physical layer network coding"
    - "physical network coding in two way wireless relay channels"

Connected through hyperedge HE-8 (induced by source paper "end to end optimized transmission over dispersive intensity modulated channels using bidirectional recurrent neural networks"):
  with members:
    - "achievable information rates for nonlinear fiber communication via end to end autoencoder learning"
    - "an introduction to deep learning for the physical layer"
    - "recurrent additive networks"
    - "applying neural networks in optical communication systems possible pitfalls"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)