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 "efficient topological layer based on persistent landscapes"):
  Abstract: We propose a novel topological layer for general deep learning models based on persistent landscapes, in which we can efficiently exploit underlying topological features of the input data structure. We use the robust DTM function and show differentiability with respect to layer inputs, for a general persistent homology with arbitrary filtration. Thus, our proposed layer can be placed anywhere in the network architecture and feed critical information on the topological features of input data into subsequent layers to improve the learnability of the networks toward a given task. A task-optimal structure of the topological layer is learned during training via backpropagation, without requiring any input featurization or data preprocessing. We provide a tight stability theorem, and show that the proposed layer is robust towards noise and outliers. We demonstrate the effectiveness of our approach by classification experiments on various datasets.

Includes members:
  Member "a topology layer for machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a topological loss function for deep learning based image segmentation using persistent homology")
  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 "connectivity optimized representation learning via persistent homology":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a topological loss function for deep learning based image segmentation using persistent homology")
  Member "on characterizing the capacity of neural networks using algebraic topology":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "geometry and topology of deep neural networks decision boundaries")
  Member "deep sets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stochastic latent residual video prediction")
  Member "topological approaches to deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a topology layer for machine learning")
  Member "a persistence landscapes toolbox for topological statistics":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "topological run time monitoring for complex systems")
  Member "deep learning with topological signatures":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep graph mapper seeing graphs through the neural lens")

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.LG(Machine Learning)