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: "on the number of response regions of deep feed forward networks with piece wise linear activations"
  Abstract: This paper explores the complexity of deep feedforward networks with linear pre-synaptic couplings and rectified linear activations. This is a contribution to the growing body of work contrasting the representational power of deep and shallow network architectures. In particular, we offer a framework for comparing deep and shallow models that belong to the family of piecewise linear functions based on computational geometry. We look at a deep rectifier multi-layer perceptron (MLP) with linear outputs units and compare it with a single layer version of the model. In the asymptotic regime, when the number of inputs stays constant, if the shallow model has $kn$ hidden units and $n_0$ inputs, then the number of linear regions is $O(k^{n_0}n^{n_0})$. For a $k$ layer model with $n$ hidden units on each layer it is $\Omega(\left\lfloor {n}/{n_0}\right\rfloor^{k-1}n^{n_0})$. The number $\left\lfloor{n}/{n_0}\right\rfloor^{k-1}$ grows faster than $k^{n_0}$ when $n$ tends to infinity or when $k$ tends to infinity and $n \geq 2n_0$. Additionally, even when $k$ is small, if we restrict $n$ to be $2n_0$, we can show that a deep model has considerably more linear regions that a shallow one. We consider this as a first step towards understanding the complexity of these models and specifically towards providing suitable mathematical tools for future analysis.

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 "deep reinforcement learning with linear quadratic regulator regions"):
  with members:
    - "deep drone racing from simulation to reality with domain randomization"
    - "solving rubik s cube with a robot hand"
    - "assessing transferability from simulation to reality for reinforcement learning"
    - "continuous control with contexts provably"
    - "learning agile and dynamic motor skills for legged robots"
    - "h_ infty model free reinforcement learning with robust stability guarantee"
    - "robust model free reinforcement learning with multi objective bayesian optimization"
    - "sensor transfer learning optimal sensor effect image augmentation for sim to real domain adaptation"

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"
    - "understanding deep neural networks with rectified linear units"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (induced by source paper "analytic marching an analytic meshing solution from deep implicit surface networks"):
  with members:
    - "orthogonal deep neural networks"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "deep level sets implicit surface representations for 3d shape inference"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
    - "blessing of dimensionality mathematical foundations of the statistical physics of data"
    - "thinet a filter level pruning method for deep neural network compression"
    - "learning both weights and connections for efficient neural networks"
    - "shapenet an information rich 3d model repository"

Connected through hyperedge HE-5 (induced by source paper "investigating the compositional structure of deep neural networks"):
  with members:
    - "deep neural network ensembles"
    - "not all samples are created equal deep learning with importance sampling"
    - "an empirical study of example forgetting during deep neural network learning"
    - "empirical bounds on linear regions of deep rectifier networks"
    - "explaining explanations an overview of interpretability of machine learning"
    - "learning what data to learn"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-6 (induced by source paper "linear predictor on linearly generated data with missing values non consistency and solutions"):
  with members:
    - "gain missing data imputation using generative adversarial nets"

Connected through hyperedge HE-7 (induced by source paper "light multi segment activation for model compression"):
  with members:
    - "model compression via distillation and quantization"
    - "private model compression via knowledge distillation"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "wrpn wide reduced precision networks"
    - "attention is all you need"
    - "apprentice using knowledge distillation techniques to improve low precision network accuracy"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "ternary neural networks with fine grained quantization"

Connected through hyperedge HE-8 (induced by source paper "network implosion effective model compression for resnets via static layer pruning and retraining"):
  with members:
    - "learning strict identity mappings in deep residual networks"
    - "data driven sparse structure selection for deep neural networks"
    - "building fast and compact convolutional neural networks for offline handwritten chinese character recognition"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"
    - "convolutional sequence to sequence learning"
    - "adaptive learning rate via covariance matrix based preconditioning for deep neural networks"
    - "learning structured sparsity in deep neural networks"
    - "identity mappings in deep residual networks"

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)