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 characterizing the capacity of neural networks using algebraic topology"
  Abstract: The learnability of different neural architectures can be characterized directly by computable measures of data complexity. In this paper, we reframe the problem of architecture selection as understanding how data determines the most expressive and generalizable architectures suited to that data, beyond inductive bias. After suggesting algebraic topology as a measure for data complexity, we show that the power of a network to express the topological complexity of a dataset in its decision boundary is a strictly limiting factor in its ability to generalize. We then provide the first empirical characterization of the topological capacity of neural networks. Our empirical analysis shows that at every level of dataset complexity, neural networks exhibit topological phase transitions and stratification. This observation allowed us to connect existing theory to empirically driven conjectures on the choice of architectures for a single hidden layer neural networks.

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"
    - "on decision regions of narrow deep neural networks"
    - "learning one hidden layer neural networks under general input distributions"

Connected through hyperedge HE-2 (induced by source paper "efficient topological layer based on persistent landscapes"):
  with members:
    - "a topology layer for machine learning"
    - "neural persistence a complexity measure for deep neural networks using algebraic topology"
    - "connectivity optimized representation learning via persistent homology"
    - "deep sets"
    - "topological approaches to deep learning"
    - "a persistence landscapes toolbox for topological statistics"
    - "deep learning with topological signatures"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"

Connected through hyperedge HE-3 (induced by source paper "gpu accelerated computation of vietoris rips persistence barcodes"):
  with members:
    - "deep learning with topological signatures"
    - "simba an efficient tool for approximating rips filtration persistence via simplicial batch collapse"
    - "a geometric perspective on sparse filtrations"
    - "the simplex tree an efficient data structure for general simplicial complexes"
    - "the compressed annotation matrix an efficient data structure for computing persistent cohomology"
    - "computing topological persistence for simplicial maps"

Connected through hyperedge HE-4 (induced by source paper "topological autoencoders"):
  with members:
    - "neural persistence a complexity measure for deep neural networks using algebraic topology"
    - "graph filtration learning"
    - "homology preserving dimensionality reduction via manifold landmarking and tearing"
    - "improved topological approximations by digitization"
    - "adam a method for stochastic optimization"
    - "linear size approximations to the vietoris rips filtration"

Connected through hyperedge HE-5 (induced by source paper "neural persistence a complexity measure for deep neural networks using algebraic topology"):
  with members:
    - "geometry score a method for comparing generative adversarial networks"
    - "methods for interpreting and understanding deep neural networks"
    - "scalable and accurate deep learning for electronic health records"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "emergence of invariance and disentanglement in deep representations"
    - "squeeze and excitation networks"
    - "opening the black box of deep neural networks via information"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-6 (induced by source paper "characterizing the shape of activation space in deep neural networks"):
  with members:
    - "topology of learning in artificial neural networks"
    - "neural persistence a complexity measure for deep neural networks using algebraic topology"
    - "adversarial examples a complete characterisation of the phenomenon"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "detecting adversarial perturbations through spatial behavior in activation spaces"
    - "improving the generalization of adversarial training with domain adaptation"

Connected through hyperedge HE-7 (induced by source paper "a topology layer for machine learning"):
  with members:
    - "topology of learning in artificial neural networks"
    - "explicit topological priors for deep learning based image segmentation using persistent homology"
    - "adversarial examples target topological holes in deep networks"
    - "neural persistence a complexity measure for deep neural networks using algebraic topology"
    - "characterizing the shape of activation space in deep neural networks"
    - "exposition and interpretation of the topology of neural networks"
    - "topology aware surface reconstruction for point clouds"
    - "adversarial attacks and defences a survey"

Connected through hyperedge HE-8 (induced by source paper "capacity allocation analysis of neural networks a tool for principled architecture design"):
  with members:
    - "capacity allocation through neural network layers"
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "deepbase deep inspection of neural networks"
    - "intriguing properties of randomly weighted networks generalizing while learning next to nothing"
    - "efficient neural audio synthesis"
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "neural audio synthesis of musical notes with wavenet autoencoders"

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)