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 "putting an end to end to end gradient isolated learning of representations"):
  Abstract: We propose a novel deep learning method for local self-supervised representation learning that does not require labels nor end-to-end backpropagation but exploits the natural order in data instead. Inspired by the observation that biological neural networks appear to learn without backpropagating a global error signal, we split a deep neural network into a stack of gradient-isolated modules. Each module is trained to maximally preserve the information of its inputs using the InfoNCE bound from Oord et al. [2018]. Despite this greedy training, we demonstrate that each module improves upon the output of its predecessor, and that the representations created by the top module yield highly competitive results on downstream classification tasks in the audio and visual domain. The proposal enables optimizing modules asynchronously, allowing large-scale distributed training of very deep neural networks on unlabelled datasets.

Includes members:
  Member "data efficient image recognition with contrastive predictive coding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning representations by predicting bags of visual words")
  Member "greedy layerwise learning can scale to imagenet":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "supervised deep similarity matching")
  Member "error forward propagation reusing feedforward connections to propagate errors in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "accelerated cnn training through gradient approximation")
  Member "auto encoding total correlation explanation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "semi supervised disentanglement with independent vector variational autoencoders")
  Member "i revnet deep invertible networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "explaining knowledge distillation by quantifying the knowledge")
  Member "information theoretic co training":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "calibration entropy rates and memory in language models")
  Member "conducting credit assignment by aligning local representations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "supervised deep similarity matching")

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)