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: "neural persistence a complexity measure for deep neural networks using algebraic topology"
  Abstract: While many approaches to make neural networks more fathomable have been proposed, they are restricted to interrogating the network with input data. Measures for characterizing and monitoring structural properties, however, have not been developed. In this work, we propose neural persistence, a complexity measure for neural network architectures based on topological data analysis on weighted stratified graphs. To demonstrate the usefulness of our approach, we show that neural persistence reflects best practices developed in the deep learning community such as dropout and batch normalization. Moreover, we derive a neural persistence-based stopping criterion that shortens the training process while achieving comparable accuracies as early stopping based on validation loss.

Connected through hyperedge HE-1 (induced by source paper "topologically densified distributions"):
  with members:
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "a kernel theory of modern data augmentation"
    - "utilizing class information for deep network representation shaping"
    - "augment your batch better training with larger batches"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "three mechanisms of weight decay regularization"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "a topological regularizer for classifiers via persistent homology"

Connected through hyperedge HE-2 (induced by source paper "efficient topological layer based on persistent landscapes"):
  with members:
    - "a topology layer for machine learning"
    - "connectivity optimized representation learning via persistent homology"
    - "on characterizing the capacity of neural networks using algebraic topology"
    - "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 "topological autoencoders"):
  with members:
    - "graph filtration learning"
    - "homology preserving dimensionality reduction via manifold landmarking and tearing"
    - "on characterizing the capacity of neural networks using algebraic topology"
    - "improved topological approximations by digitization"
    - "adam a method for stochastic optimization"
    - "linear size approximations to the vietoris rips filtration"

Connected through hyperedge HE-4 (induced by source paper "characterizing the shape of activation space in deep neural networks"):
  with members:
    - "topology of learning in artificial neural networks"
    - "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"
    - "on characterizing the capacity of neural networks using algebraic topology"
    - "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-5 (induced by source paper "pi net a deep learning approach to extract topological persistence images"):
  with members:
    - "topology of learning in artificial neural networks"
    - "exposition and interpretation of the topology of neural networks"
    - "perturbation robust representations of topological persistence diagrams"
    - "quasi periodicity quantification in video data using topology"
    - "deep learning with topological signatures"
    - "persistent homology of attractors for action recognition"
    - "a taxonomy of deep convolutional neural nets for computer vision"
    - "dense optical flow prediction from a static image"

Connected through hyperedge HE-6 (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"
    - "characterizing the shape of activation space in deep neural networks"
    - "exposition and interpretation of the topology of neural networks"
    - "on characterizing the capacity of neural networks using algebraic topology"
    - "topology aware surface reconstruction for point clouds"
    - "adversarial attacks and defences a survey"

Connected through hyperedge HE-7 (induced by source paper "graph filtration learning"):
  with members:
    - "learning metrics for persistence based summaries and applications for graph classification"
    - "hierarchical graph representation learning with differentiable pooling"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "representation learning on graphs with jumping knowledge networks"
    - "deep sets"
    - "dynamic graph cnn for learning on point clouds"
    - "how powerful are graph neural networks"
    - "a topological regularizer for classifiers via persistent homology"

Connected through hyperedge HE-8 (induced by source paper "a topological loss function for deep learning based image segmentation using persistent homology"):
  with members:
    - "phom gem persistent homology for generative models"
    - "explicit topological priors for deep learning based image segmentation using persistent homology"
    - "a topology layer for machine learning"
    - "connectivity optimized representation learning via persistent homology"
    - "toporeg a topological regularizer for classifiers"
    - "a probabilistic u net for segmentation of ambiguous images"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "beyond the pixel wise loss for topology aware delineation"

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)