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: "deep semi random features for nonlinear function approximation"
  Abstract: We propose semi-random features for nonlinear function approximation. The flexibility of semi-random feature lies between the fully adjustable units in deep learning and the random features used in kernel methods. For one hidden layer models with semi-random features, we prove with no unrealistic assumptions that the model classes contain an arbitrarily good function as the width increases (universality), and despite non-convexity, we can find such a good function (optimization theory) that generalizes to unseen new data (generalization bound). For deep models, with no unrealistic assumptions, we prove universal approximation ability, a lower bound on approximation error, a partial optimization guarantee, and a generalization bound. Depending on the problems, the generalization bound of deep semi-random features can be exponentially better than the known bounds of deep ReLU nets; our generalization error bound can be independent of the depth, the number of trainable weights as well as the input dimensionality. In experiments, we show that semi-random features can match the performance of neural networks by using slightly more units, and it outperforms random features by using significantly fewer units. Moreover, we introduce a new implicit ensemble method by using semi-random features.

Connected through hyperedge HE-1 (induced by source paper "deep randomized neural networks"):
  with members:
    - "richness of deep echo state network dynamics"
    - "what s hidden in a randomly weighted neural network"
    - "intriguing properties of randomly weighted networks generalizing while learning next to nothing"
    - "comparison between deepesns and gated rnns on multivariate time series prediction"
    - "stochastic configuration networks fundamentals and algorithms"
    - "multiplex visibility graphs to investigate recurrent neural network dynamics"
    - "deep stochastic configuration networks with universal approximation property"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"

Connected through hyperedge HE-2 (induced by source paper "compressive hyperspherical energy minimization"):
  with members:
    - "decoupled networks"
    - "group normalization"
    - "learning towards minimum hyperspherical energy"
    - "darts differentiable architecture search"
    - "diversity promoting bayesian learning of latent variable models"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep hyperspherical learning"
    - "all you need is beyond a good init exploring better solution for training extremely deep convolutional neural networks with orthonormality and modulation"

Connected through hyperedge HE-3 (induced by source paper "regret bounds for meta bayesian optimization with an unknown gaussian process prior"):
  with members:
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "a bayesian information theoretic model of bias learning"

Connected through hyperedge HE-4 (induced by source paper "mckernel approximate kernel expansions in log linear time through randomization"):
  with members:
    - "autoaugment learning augmentation policies from data"
    - "deep neural networks with multi branch architectures are less non convex"
    - "self normalizing neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "large margin object tracking with circulant feature maps"
    - "theory ii landscape of the empirical risk in deep learning"
    - "a bayesian data augmentation approach for learning deep models"
    - "learning scalable deep kernels with recurrent structure"

Connected through hyperedge HE-5 (induced by source paper "mckernel a library for approximate kernel expansions in log linear time"):
  with members:
    - "autoaugment learning augmentation policies from data"
    - "self normalizing neural networks"
    - "attention is all you need"
    - "large margin object tracking with circulant feature maps"
    - "theory ii landscape of the empirical risk in deep learning"
    - "a bayesian data augmentation approach for learning deep models"
    - "why and when can deep but not shallow networks avoid the curse of dimensionality a review"
    - "effective approaches to attention based neural machine translation"

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)