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: "deep learning with topological signatures"
  Abstract: Inferring topological and geometrical information from data can offer an alternative perspective on machine learning problems. Methods from topological data analysis, e.g., persistent homology, enable us to obtain such information, typically in the form of summary representations of topological features. However, such topological signatures often come with an unusual structure (e.g., multisets of intervals) that is highly impractical for most machine learning techniques. While many strategies have been proposed to map these topological signatures into machine learning compatible representations, they suffer from being agnostic to the target learning task. In contrast, we propose a technique that enables us to input topological signatures to deep neural networks and learn a task-optimal representation during training. Our approach is realized as a novel input layer with favorable theoretical properties. Classification experiments on 2D object shapes and social network graphs demonstrate the versatility of the approach and, in case of the latter, we even outperform the state-of-the-art by a large margin.

Connected through hyperedge HE-1 (induced by source paper "deep graph mapper seeing graphs through the neural lens"):
  with members:
    - "mincut pooling in graph neural networks"
    - "asap adaptive structure aware pooling for learning hierarchical graph representations"
    - "graph convolutional networks with eigenpooling"
    - "clique pooling for graph classification"
    - "graph u nets"
    - "on graph classification networks datasets and baselines"
    - "self attention graph pooling"
    - "graph filtration learning"

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"
    - "on characterizing the capacity of neural networks using algebraic topology"
    - "deep sets"
    - "topological approaches to deep learning"
    - "a persistence landscapes toolbox for topological statistics"
    - "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 "understanding the power of persistence pairing via permutation test"):
  with members:
    - "learning metrics for persistence based summaries and applications for graph classification"
    - "on the metric distortion of embedding persistence diagrams into separable hilbert spaces"
    - "a simple yet effective baseline for non attributed graph classification"
    - "deep sets"
    - "on the metric distortion of embedding persistence diagrams into reproducing kernel hilbert spaces"
    - "sliced wasserstein kernel for persistence diagrams"
    - "ricci curvature of the internet topology"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-4 (induced by source paper "gpu accelerated computation of vietoris rips persistence barcodes"):
  with members:
    - "on characterizing the capacity of neural networks using algebraic topology"
    - "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-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"
    - "on characterizing the capacity of neural networks using algebraic topology"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "pi net a deep learning approach to extract topological persistence images"):
  with members:
    - "topology of learning in artificial neural networks"
    - "neural persistence a complexity measure for deep neural networks using algebraic topology"
    - "exposition and interpretation of the topology of neural networks"
    - "perturbation robust representations of topological persistence diagrams"
    - "quasi periodicity quantification in video data using topology"
    - "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-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"
    - "on characterizing the capacity of neural networks using algebraic topology"
    - "topology aware surface reconstruction for point clouds"

Connected through hyperedge HE-8 (induced by source paper "shapley homology topological analysis of sample influence for neural networks"):
  with members:
    - "dataset distillation"
    - "adversarial attack on graph structured data"
    - "verification of recurrent neural networks through rule extraction"
    - "representer point selection for explaining deep neural networks"
    - "learning to reweight examples for robust deep learning"
    - "data poisoning attacks against online learning"
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "approximating the spectrum of a graph"

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.CV(Computer Vision and Pattern Recognition)