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: "understanding deep neural networks with rectified linear units"
  Abstract: In this paper we investigate the family of functions representable by deep neural networks (DNN) with rectified linear units (ReLU). We give an algorithm to train a ReLU DNN with one hidden layer to *global optimality* with runtime polynomial in the data size albeit exponential in the input dimension. Further, we improve on the known lower bounds on size (from exponential to super exponential) for approximating a ReLU deep net function by a shallower ReLU net. Our gap theorems hold for smoothly parametrized families of "hard" functions, contrary to countable, discrete families known in the literature. An example consequence of our gap theorems is the following: for every natural number $k$ there exists a function representable by a ReLU DNN with $k^2$ hidden layers and total size $k^3$, such that any ReLU DNN with at most $k$ hidden layers will require at least $\frac{1}{2}k^{k+1}-1$ total nodes. Finally, for the family of $\mathbb{R}^n\to \mathbb{R}$ DNNs with ReLU activations, we show a new lowerbound on the number of affine pieces, which is larger than previous constructions in certain regimes of the network architecture and most distinctively our lowerbound is demonstrated by an explicit construction of a *smoothly parameterized* family of functions attaining this scaling. Our construction utilizes the theory of zonotopes from polyhedral theory.

Connected through hyperedge HE-1 (induced by source paper "lossless compression of deep neural networks"):
  with members:
    - "mipaal mixed integer program as a layer"
    - "caql continuous action q learning"
    - "function approximation by deep networks"
    - "equivalent and approximate transformations of deep neural networks"
    - "differentiable convex optimization layers"
    - "accelerating primal solution findings for mixed integer programs based on solution prediction"
    - "reinforcement learning for integer programming learning to cut"
    - "machine learning for combinatorial optimization a methodological tour d horizon"

Connected through hyperedge HE-2 (induced by source paper "a deep conditioning treatment of neural networks"):
  with members:
    - "backward feature correction how deep learning performs deep learning"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "two models of double descent for weak features"
    - "size free generalization bounds for convolutional neural networks"
    - "an improved analysis of training over parameterized deep neural networks"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "implicit regularization in deep matrix factorization"
    - "on the learnability of deep random networks"

Connected through hyperedge HE-3 (induced by source paper "empirical studies on the properties of linear regions in deep neural networks"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "the shattered gradients problem if resnets are the answer then what is the question"
    - "bounding and counting linear regions of deep neural networks"
    - "towards evaluating the robustness of neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deepfool a simple and accurate method to fool deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "hyperplane arrangements of trained convnets are biased"):
  with members:
    - "geometry of deep convolutional networks"
    - "an empirical study of example forgetting during deep neural network learning"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "three factors influencing minima in sgd"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "bounding and counting linear regions of deep neural networks"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-5 (induced by source paper "neural networks are convex regularizers exact polynomial time convex optimization formulations for two layer networks"):
  with members:
    - "how do infinite width bounded norm networks look in function space"
    - "on exact computation with an infinitely wide neural net"
    - "complexity of training relu neural networks"
    - "principled deep neural network training through linear programming"
    - "a convergence theory for deep learning via over parameterization"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "reliably learning the relu in polynomial time"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "universal function approximation on graphs using multivalued functions"):
  with members:
    - "a survey on graph kernels"
    - "graph filtration learning"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "deep learning on graphs a survey"
    - "how powerful are graph neural networks"
    - "neural network with unbounded activation functions is universal approximator"
    - "graph isomorphism in quasipolynomial time"
    - "diffusion convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "deep neural networks with trainable activations and controlled lipschitz constant"):
  with members:
    - "on instabilities of deep learning in image reconstruction does ai come at a cost"
    - "cnn based projected gradient descent for consistent ct image reconstruction"
    - "exploring generalization in deep learning"
    - "deep convolutional neural network for inverse problems in imaging"
    - "u net convolutional networks for biomedical image segmentation"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "learning activation functions to improve deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "on the space time expressivity of resnets"):
  with members:
    - "analysis of the generalization error empirical risk minimization over deep artificial neural networks overcomes the curse of dimensionality in the numerical approximation of black scholes partial differential equations"
    - "resnet with one neuron hidden layers is a universal approximator"
    - "stable architectures for deep neural networks"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"
    - "why deep neural networks for function approximation"
    - "learning functions when is deep better than shallow"
    - "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)