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: "neural joint source channel coding"
  Abstract: For reliable transmission across a noisy communication channel, classical results from information theory show that it is asymptotically optimal to separate out the source and channel coding processes. However, this decomposition can fall short in the finite bit-length regime, as it requires non-trivial tuning of hand-crafted codes and assumes infinite computational power for decoding. In this work, we propose to jointly learn the encoding and decoding processes using a new discrete variational autoencoder model. By adding noise into the latent codes to simulate the channel during training, we learn to both compress and error-correct given a fixed bit-length and computational budget. We obtain codes that are not only competitive against several separation schemes, but also learn useful robust representations of the data for downstream tasks such as classification. Finally, inference amortization yields an extremely fast neural decoder, almost an order of magnitude faster compared to standard decoding methods based on iterative belief propagation.

Connected through hyperedge HE-1 (induced by source paper "distortion agnostic deep watermarking"):
  with members:
    - "deep learning for channel coding via neural mutual information estimation"
    - "romark a robust watermarking system using adversarial training"
    - "steganogan high capacity image steganography with gans"
    - "robust invisible video watermarking with attention"
    - "adversarial training for free"
    - "stegastamp invisible hyperlinks in physical photographs"
    - "redmark framework for residual diffusion watermarking on deep networks"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "turbo autoencoder deep learning based channel codes for point to point communication channels"):
  with members:
    - "deepcode feedback codes via deep learning"
    - "deep learning methods for improved decoding of linear codes"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "end to end learning of communications systems without a channel model"
    - "an introduction to deep learning for the physical layer"
    - "don t decay the learning rate increase the batch size"
    - "scaling deep learning based decoding of polar codes via partitioning"
    - "comparative study of cnn and rnn for natural language processing"

Connected through hyperedge HE-3 (induced by source paper "on distributed quantization for classification"):
  with members:
    - "generating diverse high fidelity images with vq vae 2"
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "training and inference with integers in deep neural networks"
    - "rate distortion for model compression from theory to practice"
    - "distributed simulation and distributed inference"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "neural discrete representation learning"

Connected through hyperedge HE-4 (induced by source paper "bottlenet an end to end approach for feature compression in device edge co inference systems"):
  with members:
    - "improving device edge cooperative inference of deep learning via 2 step pruning"
    - "bottlenet a deep learning architecture for intelligent mobile cloud computing services"
    - "jointdnn an efficient training and inference engine for intelligent mobile cloud computing services"
    - "deep feature compression for collaborative object detection"
    - "jalad joint accuracy and latency aware deep structure decoupling for edge cloud execution"
    - "edge host partitioning of deep neural networks with feature space encoding for resource constrained internet of things platforms"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "model free training of end to end communication systems"):
  with members:
    - "one bit ofdm receivers via deep learning"
    - "achievable information rates for nonlinear fiber communication via end to end autoencoder learning"
    - "deep joint source channel coding for wireless image transmission"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "end to end learning of communications systems without a channel model"
    - "approximating the void learning stochastic channel models from observation with variational generative adversarial networks"
    - "a learning approach to wireless information and power transfer signal and system design"

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.LG(Machine Learning)