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 decision regions of narrow deep neural networks"
  Abstract: We show that for neural network functions that have width less or equal to the input dimension all connected components of decision regions are unbounded. The result holds for continuous and strictly monotonic activation functions as well as for ReLU activation. This complements recent results on approximation capabilities of [Hanin 2017 Approximating] and connectivity of decision regions of [Nguyen 2018 Neural] for such narrow neural networks. Further, we give an example that negatively answers the question posed in [Nguyen 2018 Neural] whether one of their main results still holds for ReLU activation. Our results are illustrated by means of numerical experiments.

Connected through hyperedge HE-1 (induced by source paper "geometry and topology of deep neural networks decision boundaries"):
  with members:
    - "understanding global loss landscape of one hidden layer relu neural networks"
    - "barcodes as summary of objective function s topology"
    - "depth creates no more spurious local minima"
    - "on connected sublevel sets in deep learning"
    - "elimination of all bad local minima in deep learning"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "learning one hidden layer neural networks under general input distributions"
    - "over parameterized deep neural networks have no strict local minima for any continuous activations"

Connected through hyperedge HE-2 (induced by source paper "on the decision boundaries of deep neural networks a tropical geometry perspective"):
  with members:
    - "on the geometry of adversarial examples"
    - "robustness via curvature regularization and vice versa"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "tropical geometry of deep neural networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "on the decision boundary of deep neural networks"
    - "structured adversarial attack towards general implementation and better interpretability"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-3 (induced by source paper "universal approximation with deep narrow networks"):
  with members:
    - "deep skinny neural networks are not universal approximators"
    - "neural networks should be wide enough to learn disconnected decision regions"
    - "resnet with one neuron hidden layers is a universal approximator"
    - "the expressive power of neural networks a view from the width"

Connected through hyperedge HE-4 (induced by source paper "empirical bounds on linear regions of deep rectifier networks"):
  with members:
    - "towards robust locally linear deep networks"
    - "equivalent and approximate transformations of deep neural networks"
    - "tropical geometry of deep neural networks"
    - "neural networks should be wide enough to learn disconnected decision regions"
    - "resnet with one neuron hidden layers is a universal approximator"
    - "provable robustness of relu networks via maximization of linear regions"
    - "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-5 (induced by source paper "resnet with one neuron hidden layers is a universal approximator"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "optimal approximation of continuous functions by very deep relu networks"
    - "are resnets provably better than linear predictors"
    - "weight sharing is crucial to succesful optimization"
    - "the loss surface of deep and wide neural networks"
    - "global optimality conditions for 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)