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 "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.

Includes members:
  Member "deepcode feedback codes via deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "computing the feedback capacity of finite state channels using reinforcement learning")
  Member "deep joint source channel coding for wireless image transmission":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep joint transmission recognition for power constrained iot devices")
  Member "discrete autoencoders for sequence models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "model based reinforcement learning for atari")
  Member "theory and experiments on vector quantized autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "minimizing the bag of ngrams difference for non autoregressive neural machine translation")
  Member "deep learning for joint source channel coding of text":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an adaptive deep learning algorithm based autoencoder for interference channels")
  Member "backpropagation through the void optimizing control variates for black box gradient estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generalizable adversarial attacks with latent variable perturbation modelling")
  Member "rebar low variance unbiased gradient estimates for discrete latent variable models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "inverse graphics gan learning to generate 3d shapes from unstructured 2d data")
  Member "infovae information maximizing variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "nestedvae isolating common factors via weak supervision")

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.LG(Machine Learning)