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 "deep learning without weight transport"):
  Abstract: Current algorithms for deep learning probably cannot run in the brain because they rely on weight transport, where forward-path neurons transmit their synaptic weights to a feedback path, in a way that is likely impossible biologically. An algorithm called feedback alignment achieves deep learning without weight transport by using random feedback weights, but it performs poorly on hard visual-recognition tasks. Here we describe two mechanisms - a neural circuit called a weight mirror and a modification of an algorithm proposed by Kolen and Pollack in 1994 - both of which let the feedback path learn appropriate synaptic weights quickly and accurately even in large networks, without weight transport or complex wiring.Tested on the ImageNet visual-recognition task, these mechanisms outperform both feedback alignment and the newer sign-symmetry method, and nearly match backprop, the standard algorithm of deep learning, which uses weight transport.

Includes members:
  Member "tf replicator distributed machine learning for researchers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial video generation on complex datasets")
  Member "unsupervised learning by competing hidden units":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "biologically motivated deep learning method using hierarchical competitive learning")
  Member "direct feedback alignment with sparse connections for local learning":
    also connected through other hyperedges:
      (none)
  Member "loss landscapes of regularized linear autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a linear systems theory of normalizing flows")
  Member "biologically plausible learning algorithms can scale to large datasets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "accelerated cnn training through gradient approximation")
  Member "feedback alignment in deep convolutional networks":
    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 "assessing the scalability of biologically motivated deep learning algorithms and architectures":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "revisiting spatial invariance with low rank local connectivity")
  Member "understanding reuse performance and hardware cost of dnn dataflows a data centric approach":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "autodnnchip an automated dnn chip predictor and builder for both fpgas and asics")

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)