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: "to go deep or wide in learning"
  Abstract: To achieve acceptable performance for AI tasks, one can either use sophisticated feature extraction methods as the first layer in a two-layered supervised learning model, or learn the features directly using a deep (multi-layered) model. While the first approach is very problem-specific, the second approach has computational overheads in learning multiple layers and fine-tuning of the model. In this paper, we propose an approach called wide learning based on arc-cosine kernels, that learns a single layer of infinite width. We propose exact and inexact learning strategies for wide learning and show that wide learning with single layer outperforms single layer as well as deep architectures of finite width for some benchmark datasets.

Connected through hyperedge HE-1 (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"
    - "revisit fuzzy neural network demystifying batch normalization and relu with generalized hamming network"
    - "a geometric view of optimal transportation and generative model"
    - "visualizing the loss landscape of neural nets"

Connected through hyperedge HE-2 (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"
    - "revisit fuzzy neural network demystifying batch normalization and relu with generalized hamming network"
    - "sharp minima can generalize for deep nets"

Connected through hyperedge HE-3 (induced by source paper "invariance of weight distributions in rectified mlps"):
  with members:
    - "rethinking generalization requires revisiting old ideas statistical mechanics approaches and complex learning behavior"
    - "the shattered gradients problem if resnets are the answer then what is the question"
    - "opening the black box of deep neural networks via information"
    - "understanding deep learning requires rethinking generalization"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"
    - "wavenet a generative model for raw audio"
    - "all you need is a good init"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)