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 "improving discrete latent representations with differentiable approximation bridges"):
  Abstract: Modern neural network training relies on piece-wise (sub-)differentiable functions in order to use backpropagation to update model parameters. In this work, we introduce a novel method to allow simple non-differentiable functions at intermediary layers of deep neural networks. We do so by training with a differentiable approximation bridge (DAB) neural network which approximates the non-differentiable forward function and provides gradient updates during backpropagation. We present strong empirical results (performing over 600 experiments) in four different domains: unsupervised (image) representation learning, variational (image) density estimation, image classification, and sequence sorting to demonstrate that our proposed method improves state of the art performance. We demonstrate that training with DAB aided discrete non-differentiable functions improves image reconstruction quality and posterior linear separability by 10% against the Gumbel-Softmax relaxed estimator [37, 26] as well as providing a 9% improvement in the test variational lower bound in comparison to the state of the art RELAX [16] discrete estimator. We also observe an accuracy improvement of 77% in neural sequence sorting and a 25% improvement against the straight-through estimator [5] in an image classification setting. The DAB network is not used for inference and expands the class of functions that are usable in neural networks.

Includes members:
  Member "decoupled greedy learning of cnns":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sideways depth parallel training of video models")
  Member "a new benchmark and progress toward improved weakly supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "variational saccading efficient inference for large resolution images")
  Member "deep learning using rectified linear units relu":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hyper parameter optimization a review of algorithms and applications")
  Member "challenging common assumptions in the unsupervised learning of disentangled representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "neural arithmetic logic units":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stealing black box functionality using the deep neural tree architecture")
  Member "training neural networks using features replay":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sideways depth parallel training of video models")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "backpropagation through the void optimizing control variates for black box gradient estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generalizable adversarial attacks with latent variable perturbation modelling")

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)