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: "error forward propagation reusing feedforward connections to propagate errors in deep learning"
  Abstract: We introduce Error Forward-Propagation, a biologically plausible mechanism to propagate error feedback forward through the network. Architectural constraints on connectivity are virtually eliminated for error feedback in the brain; systematic backward connectivity is not used or needed to deliver error feedback. Feedback as a means of assigning credit to neurons earlier in the forward pathway for their contribution to the final output is thought to be used in learning in the brain. How the brain solves the credit assignment problem is unclear. In machine learning, error backpropagation is a highly successful mechanism for credit assignment in deep multilayered networks. Backpropagation requires symmetric reciprocal connectivity for every neuron. From a biological perspective, there is no evidence of such an architectural constraint, which makes backpropagation implausible for learning in the brain. This architectural constraint is reduced with the use of random feedback weights. Models using random feedback weights require backward connectivity patterns for every neuron, but avoid symmetric weights and reciprocal connections. In this paper, we practically remove this architectural constraint, requiring only a backward loop connection for effective error feedback. We propose reusing the forward connections to deliver the error feedback by feeding the outputs into the input receiving layer. This mechanism, Error Forward-Propagation, is a plausible basis for how error feedback occurs deep in the brain independent of and yet in support of the functionality underlying intricate network architectures. We show experimentally that recurrent neural networks with two and three hidden layers can be trained using Error Forward-Propagation on the MNIST and Fashion MNIST datasets, achieving $1.90\%$ and $11\%$ generalization errors respectively.

Connected through hyperedge HE-1 (induced by source paper "biologically plausible deep learning but how far can we go with shallow networks"):
  with members:
    - "unsupervised learning by competing hidden units"
    - "deep learning in spiking neural networks"
    - "putting a bug in ml the moth olfactory network learns to read mnist"
    - "feedback alignment in deep convolutional networks"
    - "regularized evolution for image classifier architecture search"
    - "a biologically plausible learning rule for deep learning in the brain"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "direct training for spiking neural networks faster larger better"

Connected through hyperedge HE-2 (induced by source paper "the hsic bottleneck deep learning without back propagation"):
  with members:
    - "on mutual information maximization for representation learning"
    - "biologically plausible learning algorithms can scale to large datasets"
    - "feedback alignment in deep convolutional networks"
    - "a biologically plausible learning rule for deep learning in the brain"
    - "intriguing properties of randomly weighted networks generalizing while learning next to nothing"
    - "gradient target propagation"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "dependency bottleneck in auto encoding architectures an empirical study"

Connected through hyperedge HE-3 (induced by source paper "putting an end to end to end gradient isolated learning of representations"):
  with members:
    - "data efficient image recognition with contrastive predictive coding"
    - "greedy layerwise learning can scale to imagenet"
    - "biologically plausible learning algorithms can scale to large datasets"
    - "auto encoding total correlation explanation"
    - "i revnet deep invertible networks"
    - "information theoretic co training"
    - "conducting credit assignment by aligning local representations"
    - "representation learning with contrastive predictive coding"

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.NE(Neural and Evolutionary Computing)