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 "assessing the scalability of biologically motivated deep learning algorithms and architectures"):
  Abstract: The backpropagation of error algorithm (BP) is impossible to implement in a real brain. The recent success of deep networks in machine learning and AI, however, has inspired proposals for understanding how the brain might learn across multiple layers, and hence how it might approximate BP. As of yet, none of these proposals have been rigorously evaluated on tasks where BP-guided deep learning has proved critical, or in architectures more structured than simple fully-connected networks. Here we present results on scaling up biologically motivated models of deep learning on datasets which need deep networks with appropriate architectures to achieve good performance. We present results on the MNIST, CIFAR-10, and ImageNet datasets and explore variants of target-propagation (TP) and feedback alignment (FA) algorithms, and explore performance in both fully- and locally-connected architectures. We also introduce weight-transport-free variants of difference target propagation (DTP) modified to remove backpropagation from the penultimate layer. Many of these algorithms perform well for MNIST, but for CIFAR and ImageNet we find that TP and FA variants perform significantly worse than BP, especially for networks composed of locally connected units, opening questions about whether new architectures and algorithms are required to scale these approaches. Our results and implementation details help establish baselines for biologically motivated deep learning schemes going forward.

Includes members:
  Member "biologically motivated algorithms for propagating local target representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "supervised deep similarity matching")
  Member "conducting credit assignment by aligning local representations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "supervised deep similarity matching")
  Member "feedforward initialization for fast inference of deep generative networks is biologically plausible":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a framework for searching for general artificial intelligence")
  Member "equilibrium propagation bridging the gap between energy based models and backpropagation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning in memristive nanowire networks")
  Member "early inference in energy based models approximates back propagation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep neural networks abstract like humans")
  Member "towards biologically plausible deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sideways depth parallel training of video models")
  Member "how auto encoders could provide credit assignment in deep networks via target propagation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "biologically motivated deep learning method using hierarchical competitive learning")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)