human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "pi net a deep learning approach to extract topological persistence images"):
  Abstract: Topological features such as persistence diagrams and their functional approximations like persistence images (PIs) have been showing substantial promise for machine learning and computer vision applications. Key bottlenecks to their large scale adoption are computational expenditure and difficulty in incorporating them in a differentiable architecture. We take an important step in this paper to mitigate these bottlenecks by proposing a novel one-step approach to generate PIs directly from the input data. We propose a simple convolutional neural network architecture called PI-Net that allows us to learn mappings between the input data and PIs. We design two separate architectures, one designed to take in multi-variate time series signals as input and another that accepts multi-channel images as input. We call these networks Signal PI-Net and Image PI-Net respectively. To the best of our knowledge, we are the first to propose the use of deep learning for computing topological features directly from data. We explore the use of the proposed method on two applications: human activity recognition using accelerometer sensor data and image classification. We demonstrate the ease of fusing PIs in supervised deep learning architectures and speed up of several orders of magnitude for extracting PIs from data. Our code is available at this https URL.

Includes members:
  Member "topology of learning in artificial neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "classifying the classifier dissecting the weight space of neural networks")
  Member "neural persistence a complexity measure for deep neural networks using algebraic topology":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "topologically densified distributions")
  Member "exposition and interpretation of the topology of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a topology layer for machine learning")
  Member "perturbation robust representations of topological persistence diagrams":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "understanding the topology and the geometry of the persistence diagram space via optimal partial transport")
  Member "quasi periodicity quantification in video data using topology":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hand tremor frequency estimation in videos")
  Member "deep learning with topological signatures":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep graph mapper seeing graphs through the neural lens")
  Member "persistent homology of attractors for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "efficient topological layer based on persistent landscapes")
  Member "a taxonomy of deep convolutional neural nets for computer vision":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "spatio semantic convnet based visual place recognition")

Question: Please tell me which class the source paper 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)