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: "revisit fuzzy neural network demystifying batch normalization and relu with generalized hamming network"
  Abstract: We revisit fuzzy neural network with a cornerstone notion of generalized hamming distance, which provides a novel and theoretically justified framework to re-interpret many useful neural network techniques in terms of fuzzy logic. In particular, we conjecture and empirically illustrate that, the celebrated batch normalization (BN) technique actually adapts the normalized bias such that it approximates the rightful bias induced by the generalized hamming distance. Once the due bias is enforced analytically, neither the optimization of bias terms nor the sophisticated batch normalization is needed. Also in the light of generalized hamming distance, the popular rectified linear units (ReLU) can be treated as setting a minimal hamming distance threshold between network inputs and weights. This thresholding scheme, on the one hand, can be improved by introducing double thresholding on both extremes of neuron outputs. On the other hand, ReLUs turn out to be non-essential and can be removed from networks trained for simple tasks like MNIST classification. The proposed generalized hamming network (GHN) as such not only lends itself to rigorous analysis and interpretation within the fuzzy logic theory but also demonstrates fast learning speed, well-controlled behaviour and state-of-the-art performances on a variety of learning tasks.

Connected through hyperedge HE-1 (induced by source paper "optimize tsk fuzzy systems for classification problems mini batch gradient descent with uniform regularization and batch normalization"):
  with members:
    - "mixture models for diverse machine translation tricks of the trade"
    - "on the functional equivalence of tsk fuzzy systems to neural networks mixture of experts cart and stacking ensemble regression"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "group normalization"
    - "revisiting small batch training for deep neural networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "improving generalization performance by switching from adam to sgd"
    - "a survey on soft subspace clustering"

Connected through hyperedge HE-2 (induced by source paper "optimize tsk fuzzy systems for big data classification problems bag of tricks"):
  with members:
    - "mixture models for diverse machine translation tricks of the trade"
    - "on the functional equivalence of tsk fuzzy systems to neural networks mixture of experts cart and stacking ensemble regression"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "group normalization"
    - "revisiting small batch training for deep neural networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "improving generalization performance by switching from adam to sgd"
    - "densely connected convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "fuzzy logic interpretation of artificial neural networks"):
  with members:
    - "geometric understanding of deep learning"
    - "structurally sensitive multi scale deep neural network for low dose ct denoising"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
    - "a new type of neurons for machine learning"
    - "interpreting deep visual representations via network dissection"
    - "a geometric view of optimal transportation and generative model"
    - "visualizing the loss landscape of neural nets"
    - "sharp minima can generalize for deep nets"

Connected through hyperedge HE-4 (induced by source paper "fuzzy logic interpretation of quadratic networks"):
  with members:
    - "geometric understanding of deep learning"
    - "structurally sensitive multi scale deep neural network for low dose ct denoising"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "a new type of neurons for machine learning"
    - "interpreting deep visual representations via network dissection"
    - "sharp minima can generalize for deep nets"
    - "the loss surface of deep and wide neural networks"

Connected through hyperedge HE-5 (induced by source paper "soft autoencoder and its wavelet shrinkage interpretation"):
  with members:
    - "competitive performance of a modularized deep neural network compared to commercial algorithms for low dose ct image reconstruction"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
    - "fuzzy logic interpretation of artificial neural networks"
    - "3 d convolutional encoder decoder network for low dose ct via transfer learning from a 2 d trained network"
    - "fastmri an open dataset and benchmarks for accelerated mri"
    - "a new type of neurons for machine learning"
    - "a deep convolutional auto encoder with pooling unpooling layers in caffe"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"

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)