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: "eignets for function approximation on manifolds"
  Abstract: Let $\XX$ be a compact, smooth, connected, Riemannian manifold without boundary, $G:\XX\times\XX\to \RR$ be a kernel. Analogous to a radial basis function network, an eignet is an expression of the form $\sum_{j=1}^M a_jG(\circ,y_j)$, where $a_j\in\RR$, $y_j\in\XX$, $1\le j\le M$. We describe a deterministic, universal algorithm for constructing an eignet for approximating functions in $L^p(\mu;\XX)$ for a general class of measures $\mu$ and kernels $G$. Our algorithm yields linear operators. Using the minimal separation amongst the centers $y_j$ as the cost of approximation, we give modulus of smoothness estimates for the degree of approximation by our eignets, and show by means of a converse theorem that these are the best possible for every \emph{individual function}. We also give estimates on the coefficients $a_j$ in terms of the norm of the eignet. Finally, we demonstrate that if any sequence of eignets satisfies the optimal estimates for the degree of approximation of a smooth function, measured in terms of the minimal separation, then the derivatives of the eignets also approximate the corresponding derivatives of the target function in an optimal manner.

Connected through hyperedge HE-1 (induced by source paper "function approximation by deep networks"):
  with members:
    - "dimension independent bounds for general shallow networks"
    - "construction of neural networks for realization of localized deep learning"
    - "optimal approximation of continuous functions by very deep relu networks"
    - "on the expressive power of overlapping architectures of deep learning"
    - "function approximation with zonal function networks with activation functions analogous to the rectified linear unit functions"
    - "bounding and counting linear regions of deep neural networks"
    - "error bounds for approximations with deep relu networks"
    - "deep vs shallow networks an approximation theory perspective"

Connected through hyperedge HE-2 (induced by source paper "dimension independent bounds for general shallow networks"):
  with members:
    - "optimal approximation of continuous functions by very deep relu networks"
    - "an analysis of training and generalization errors in shallow and deep networks"
    - "function approximation with zonal function networks with activation functions analogous to the rectified linear unit functions"
    - "deep vs shallow networks an approximation theory perspective"
    - "why and when can deep but not shallow networks avoid the curse of dimensionality a review"
    - "breaking the curse of dimensionality with convex neural networks"

Connected through hyperedge HE-3 (induced by source paper "a witness function based construction of discriminative models using hermite polynomials"):
  with members:
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "on calibration of modern neural networks"
    - "a deep learning approach to diabetic blood glucose prediction"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "an analysis of training and generalization errors in shallow and deep networks"):
  with members:
    - "theory iiib generalization in deep networks"
    - "exploring generalization in deep learning"
    - "function approximation with zonal function networks with activation functions analogous to the rectified linear unit functions"
    - "theory of deep learning iii explaining the non overfitting puzzle"
    - "a deep learning approach to diabetic blood glucose prediction"
    - "understanding deep learning requires rethinking generalization"
    - "deep vs shallow networks an approximation theory perspective"
    - "train faster generalize better stability of stochastic gradient descent"

Connected through hyperedge HE-5 (induced by source paper "function approximation with zonal function networks with activation functions analogous to the rectified linear unit functions"):
  with members:
    - "error bounds for approximations with deep relu networks"
    - "deep vs shallow networks an approximation theory perspective"
    - "the loss surfaces of multilayer networks"
    - "breaking the curse of dimensionality with convex neural networks"

Connected through hyperedge HE-6 (induced by source paper "a deep learning approach to diabetic blood glucose prediction"):
  with members:
    - "deep vs shallow networks an approximation theory perspective"
    - "representation benefits of deep feedforward networks"

Connected through hyperedge HE-7 (induced by source paper "deep vs shallow networks an approximation theory perspective"):
  with members:
    - "learning functions when is deep better than shallow"
    - "the power of depth for feedforward neural networks"
    - "representation benefits of deep feedforward networks"
    - "deep residual learning for image recognition"

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)