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: "tropical geometry of deep neural networks"
  Abstract: We establish, for the first time, connections between feedforward neural networks with ReLU activation and tropical geometry --- we show that the family of such neural networks is equivalent to the family of tropical rational maps. Among other things, we deduce that feedforward ReLU neural networks with one hidden layer can be characterized by zonotopes, which serve as building blocks for deeper networks; we relate decision boundaries of such neural networks to tropical hypersurfaces, a major object of study in tropical geometry; and we prove that linear regions of such neural networks correspond to vertices of polytopes associated with tropical rational functions. An insight from our tropical formulation is that a deeper network is exponentially more expressive than a shallow network.

Connected through hyperedge HE-1 (induced by source paper "on the decision boundaries of deep neural networks a tropical geometry perspective"):
  with members:
    - "on the geometry of adversarial examples"
    - "on decision regions of narrow deep neural networks"
    - "robustness via curvature regularization and vice versa"
    - "the lottery ticket hypothesis finding sparse trainable 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-2 (induced by source paper "tropical polynomial division and neural networks"):
  with members:
    - "a universal approximation result for difference of log sum exp neural networks"
    - "log sum exp neural networks and posynomial models for convex and log log convex data"
    - "thinet a filter level pruning method for deep neural network compression"
    - "channel pruning for accelerating very deep neural networks"
    - "dynamical systems on weighted lattices general theory"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-3 (induced by source paper "tropical geometry and piecewise linear approximation of curves and surfaces on weighted lattices"):
  with members:
    - "a universal approximation result for difference of log sum exp neural networks"
    - "log sum exp neural networks and posynomial models for convex and log log convex data"
    - "dynamical systems on weighted lattices general theory"
    - "ensemble methods for convex regression with applications to geometric programming based circuit design"

Connected through hyperedge HE-4 (induced by source paper "a universal approximation result for difference of log sum exp neural networks"):
  with members:
    - "log sum exp neural networks and posynomial models for convex and log log convex data"

Connected through hyperedge HE-5 (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"
    - "on decision regions of narrow 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-6 (induced by source paper "geometry of deep convolutional networks"):
  with members:
    - "efficient representation of low dimensional manifolds using deep networks"
    - "understanding deep neural networks with rectified linear units"
    - "visualizing deep convolutional neural networks using natural pre images"
    - "inverting visual representations with convolutional networks"
    - "understanding deep image representations by inverting them"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "log sum exp neural networks and posynomial models for convex and log log convex data"):
  with members:
    - "sparse identification of posynomial models"

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)