human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "how auto encoders could provide credit assignment in deep networks via target propagation"
  Abstract: We propose to exploit {\em reconstruction} as a layer-local training signal for deep learning. Reconstructions can be propagated in a form of target propagation playing a role similar to back-propagation but helping to reduce the reliance on derivatives in order to perform credit assignment across many levels of possibly strong non-linearities (which is difficult for back-propagation). A regularized auto-encoder tends produce a reconstruction that is a more likely version of its input, i.e., a small move in the direction of higher likelihood. By generalizing gradients, target propagation may also allow to train deep networks with discrete hidden units. If the auto-encoder takes both a representation of input and target (or of any side information) in input, then its reconstruction of input representation provides a target towards a representation that is more likely, conditioned on all the side information. A deep auto-encoder decoding path generalizes gradient propagation in a learned way that can could thus handle not just infinitesimal changes but larger, discrete changes, hopefully allowing credit assignment through a long chain of non-linear operations. In addition to each layer being a good auto-encoder, the encoder also learns to please the upper layers by transforming the data into a space where it is easier to model by them, flattening manifolds and disentangling factors. The motivations and theoretical justifications for this approach are laid down in this paper, along with conjectures that will have to be verified either mathematically or experimentally, including a hypothesis stating that such auto-encoder mediated target propagation could play in brains the role of credit assignment through many non-linear, noisy and discrete transformations.

Connected through hyperedge HE-1 (induced by source paper "biologically motivated deep learning method using hierarchical competitive learning"):
  with members:
    - "unsupervised learning by competing hidden units"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "competitive learning enriches learning representation and accelerates the fine tuning of cnns"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "going deeper with convolutions"
    - "difference target propagation"
    - "winner take all autoencoders"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-2 (induced by source paper "unsupervised progressive learning and the stam architecture"):
  with members:
    - "lifelong machine learning with deep streaming linear discriminant analysis"
    - "continual lifelong learning with neural networks a review"
    - "continual learning via neural pruning"
    - "three scenarios for continual learning"
    - "continual learning with tiny episodic memories"
    - "centroid networks for few shot clustering and unsupervised few shot classification"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"

Connected through hyperedge HE-3 (induced by source paper "temporal coding in spiking neural networks with alpha synaptic function"):
  with members:
    - "deep learning in spiking neural networks"
    - "enabling spike based backpropagation in state of the art deep neural network architectures"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "spiking deep residual network"
    - "evolutionary neural hybrid agents for architecture search"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "spatio temporal backpropagation for training high performance spiking neural networks"

Connected through hyperedge HE-4 (induced by source paper "associated learning decomposing end to end backpropagation based on auto encoders and target propagation"):
  with members:
    - "decoupled parallel backpropagation with convergence guarantee"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "training neural networks using features replay"
    - "biologically motivated algorithms for propagating local target representations"
    - "conducting credit assignment by aligning local representations"
    - "deep supervised learning using local errors"
    - "dynamic routing between capsules"
    - "decoupled neural interfaces using synthetic gradients"

Connected through hyperedge HE-5 (induced by source paper "dante deep alternations for training neural networks"):
  with members:
    - "learning relu networks via alternating minimization"
    - "a provably correct algorithm for deep learning that actually works"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "recovery guarantees for one hidden layer neural networks"
    - "failures of gradient based deep learning"
    - "decoupled neural interfaces using synthetic gradients"
    - "training neural networks without gradients a scalable admm approach"
    - "efficient approaches for escaping higher order saddle points in non convex optimization"

Connected through hyperedge HE-6 (induced by source paper "reconstruction of hidden representation for robust feature extraction"):
  with members:
    - "a survey on deep learning in medical image analysis"
    - "deconstructing the ladder network architecture"
    - "made masked autoencoder for distribution estimation"
    - "semi supervised learning with ladder networks"
    - "rectified factor networks"
    - "deep generative stochastic networks trainable by backprop"
    - "deep learning of representations looking forward"
    - "generalized denoising auto encoders as generative models"

Connected through hyperedge HE-7 (induced by source paper "robust nonlinear component estimation with tikhonov regularization"):
  with members:
    - "density modeling of images using a generalized normalization transformation"
    - "nice non linear independent components estimation"
    - "a unifying probabilistic perspective for spectral dimensionality reduction insights and new models"

Connected through hyperedge HE-8 (induced by source paper "understanding autoencoders with information theoretic concepts"):
  with members:
    - "understanding deep learning generalization by maximum entropy"
    - "an introduction to deep learning for the physical layer"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "interpreting cnn knowledge via an explanatory graph"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "opening the black box of deep neural networks via information"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "estimating mixture entropy with pairwise distances"

Question: Please tell me which class the center node 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)