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 "associated learning decomposing end to end backpropagation based on auto encoders and target propagation"):
  Abstract: Backpropagation has been widely used in deep learning approaches, but it is inefficient and sometimes unstable because of backward locking and vanishing/exploding gradient problems, especially when the gradient flow is long. Additionally, updating all edge weights based on a single objective seems biologically implausible. In this paper, we introduce a novel biologically motivated learning structure called Associated Learning, which modularizes the network into smaller components, each of which has a local objective. Because the objectives are mutually independent, Associated Learning can learn the parameters independently and simultaneously when these parameters belong to different components. Surprisingly, training deep models by Associated Learning yields comparable accuracies to models trained using typical backpropagation methods, which aims at fitting the target variable directly. Moreover, probably because the gradient flow of each component is short, deep networks can still be trained with Associated Learning even when some of the activation functions are sigmoid-a situation that usually results in the vanishing gradient problem when using typical backpropagation. We also found that the Associated Learning generates better metafeatures, which we demonstrated both quantitatively (via inter-class and intra-class distance comparisons in the hidden layers) and qualitatively (by visualizing the hidden layers using t-SNE).

Includes members:
  Member "decoupled parallel backpropagation with convergence guarantee":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ouroboros on accelerating training of transformer based language models")
  Member "assessing the scalability of biologically motivated deep learning algorithms and architectures":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "revisiting spatial invariance with low rank local connectivity")
  Member "training neural networks using features replay":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sideways depth parallel training of video models")
  Member "biologically motivated algorithms for propagating local target representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "supervised deep similarity matching")
  Member "conducting credit assignment by aligning local representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "supervised deep similarity matching")
  Member "deep supervised learning using local errors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spiking neural networks for inference and learning a memristor based design perspective")
  Member "dynamic routing between capsules":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lightweight residual densely connected convolutional neural network")
  Member "decoupled neural interfaces using synthetic gradients":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sideways depth parallel training of video models")

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.NE(Neural and Evolutionary Computing)