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 "the hsic bottleneck deep learning without back propagation"):
  Abstract: We introduce the HSIC (Hilbert-Schmidt independence criterion) bottleneck for training deep neural networks. The HSIC bottleneck is an alternative to the conventional cross-entropy loss and backpropagation that has a number of distinct advantages. It mitigates exploding and vanishing gradients, resulting in the ability to learn very deep networks without skip connections. There is no requirement for symmetric feedback or update locking. We find that the HSIC bottleneck provides performance on MNIST/FashionMNIST/CIFAR10 classification comparable to backpropagation with a cross-entropy target, even when the system is not encouraged to make the output resemble the classification labels. Appending a single layer trained with SGD (without backpropagation) to reformat the information further improves performance.

Includes members:
  Member "on mutual information maximization for representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "error forward propagation reusing feedforward connections to propagate errors in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "biologically plausible deep learning but how far can we go with shallow networks")
  Member "biologically plausible learning algorithms can scale to large datasets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "accelerated cnn training through gradient approximation")
  Member "feedback alignment in deep convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "biologically plausible deep learning but how far can we go with shallow networks")
  Member "a biologically plausible learning rule for deep learning in the brain":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "biologically plausible deep learning but how far can we go with shallow networks")
  Member "intriguing properties of randomly weighted networks generalizing while learning next to nothing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "training batchnorm and only batchnorm on the expressive power of random features in cnns")
  Member "gradient target propagation":
    also connected through other hyperedges:
      (none)
  Member "assessing the scalability of biologically motivated deep learning algorithms and architectures":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "revisiting spatial invariance with low rank local connectivity")

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)