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 "understanding the power of persistence pairing via permutation test"):
  Abstract: Recently many efforts have been made to incorporate persistence diagrams, one of the major tools in topological data analysis (TDA), into machine learning pipelines. To better understand the power and limitation of persistence diagrams, we carry out a range of experiments on both graph data and shape data, aiming to decouple and inspect the effects of different factors involved. To this end, we also propose the so-called \emph{permutation test} for persistence diagrams to delineate critical values and pairings of critical values. For graph classification tasks, we note that while persistence pairing yields consistent improvement over various benchmark datasets, it appears that for various filtration functions tested, most discriminative power comes from critical values. For shape segmentation and classification, however, we note that persistence pairing shows significant power on most of the benchmark datasets, and improves over both summaries based on merely critical values, and those based on permutation tests. Our results help provide insights on when persistence diagram based summaries could be more suitable.

Includes members:
  Member "learning metrics for persistence based summaries and applications for graph classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "graph filtration learning")
  Member "on the metric distortion of embedding persistence diagrams into separable hilbert spaces":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "persistence b spline grids stable vector representation of persistence diagrams based on data fitting")
  Member "a simple yet effective baseline for non attributed graph classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "just slaq when you approximate accurate spectral distances for web scale graphs")
  Member "deep sets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stochastic latent residual video prediction")
  Member "on the metric distortion of embedding persistence diagrams into reproducing kernel hilbert spaces":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approximating continuous functions on persistence diagrams using template functions")
  Member "sliced wasserstein kernel for persistence diagrams":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "progressive wasserstein barycenters of persistence diagrams")
  Member "deep learning with topological signatures":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep graph mapper seeing graphs through the neural lens")
  Member "ricci curvature of the internet topology":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "computationally tractable riemannian manifolds for graph embeddings")

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)