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: "bounding and counting linear regions of deep neural networks"
  Abstract: We investigate the complexity of deep neural networks (DNN) that represent piecewise linear (PWL) functions. In particular, we study the number of linear regions, i.e. pieces, that a PWL function represented by a DNN can attain, both theoretically and empirically. We present (i) tighter upper and lower bounds for the maximum number of linear regions on rectifier networks, which are exact for inputs of dimension one; (ii) a first upper bound for multi-layer maxout networks; and (iii) a first method to perform exact enumeration or counting of the number of regions by modeling the DNN with a mixed-integer linear formulation. These bounds come from leveraging the dimension of the space defining each linear region. The results also indicate that a deep rectifier network can only have more linear regions than every shallow counterpart with same number of neurons if that number exceeds the dimension of the input.

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 "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"
    - "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"
    - "deepfool a simple and accurate method to fool deep neural networks"

Connected through hyperedge HE-3 (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"
    - "understanding deep learning requires rethinking generalization"
    - "understanding deep neural networks with rectified linear units"

Connected through hyperedge HE-4 (induced by source paper "reachability analysis for feed forward neural networks using face lattices"):
  with members:
    - "formal security analysis of neural networks using symbolic intervals"
    - "formal verification of cnn based perception systems"
    - "efficient neural network robustness certification with general activation functions"
    - "efficient formal safety analysis of neural networks"
    - "reachable set computation and safety verification for neural networks with relu activations"
    - "an approach to reachability analysis for feed forward relu neural networks"
    - "output range analysis for deep neural networks"
    - "reluplex an efficient smt solver for verifying deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "equivalent and approximate transformations of deep neural networks"):
  with members:
    - "quantized densely connected u nets for efficient landmark localization"
    - "learning strict identity mappings in deep residual networks"
    - "training for faster adversarial robustness verification via inducing relu stability"
    - "resnet with one neuron hidden layers is a universal approximator"
    - "empirical bounds on linear regions of deep rectifier networks"
    - "deep networks with probabilistic gates"
    - "deep neural networks as 0 1 mixed integer linear programs a feasibility study"
    - "evaluating robustness of neural networks with mixed integer programming"

Connected through hyperedge HE-6 (induced by source paper "caql continuous action q learning"):
  with members:
    - "equivalent and approximate transformations of deep neural networks"
    - "challenges of real world reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "horizon facebook s open source applied reinforcement learning platform"
    - "a lyapunov based approach to safe reinforcement learning"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-7 (induced by source paper "towards robust locally linear deep networks"):
  with members:
    - "on the convergence of adam and beyond"
    - "scaling provable adversarial defenses"
    - "provable robustness of relu networks via maximization of linear regions"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "deep defense training dnns with improved adversarial robustness"
    - "on the robustness of interpretability methods"
    - "deep neural networks as 0 1 mixed integer linear programs a feasibility study"
    - "smoothgrad removing noise by adding noise"

Connected through hyperedge HE-8 (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"
    - "error bounds for approximations with deep relu networks"
    - "deep vs shallow networks an approximation theory perspective"
    - "benefits of depth in neural 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)