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 "conducting credit assignment by aligning local representations"):
  Abstract: The use of back-propagation and its variants to train deep networks is often problematic for new users, with issues such as exploding gradients, vanishing gradients, and high sensitivity to weight initialization strategies often making networks difficult to train. In this paper, we present Local Representation Alignment (LRA), a training procedure that is much less sensitive to bad initializations, does not require modifications to the network architecture, and can be adapted to networks with highly nonlinear and discrete-valued activation functions. Furthermore, we show that one variation of LRA can start with a null initialization of network weights and still successfully train networks with a wide variety of nonlinearities, including tanh, ReLU-6, softplus, signum and others that are more biologically plausible. #R##N#Experiments on MNIST and Fashion MNIST validate the performance of the algorithm and show that LRA can train networks robustly and effectively, succeeding even when back-propagation fails and outperforming other alternative learning algorithms, such as target propagation and feedback alignment.

Includes members:
  Member "learning to adapt by minimizing discrepancy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spiking neural predictive coding for continual learning from data streams")
  Member "fashion mnist a novel image dataset for benchmarking machine learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "decoupled neural interfaces using synthetic gradients":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sideways depth parallel training of video models")
  Member "piecewise latent variables for neural variational text processing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "non autoregressive neural dialogue generation")
  Member "a modular theory of feature learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to adapt by minimizing discrepancy")
  Member "all you need is a good init":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "regularizing activations in neural networks via distribution matching with the wasserstein metric")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "batch normalization accelerating deep network training by reducing internal covariate shift":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)