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 "riemannian batch normalization for spd neural networks"):
  Abstract: Covariance matrices have attracted attention for machine learning applications due to their capacity to capture interesting structure in the data. The main challenge is that one needs to take into account the particular geometry of the Riemannian manifold of symmetric positive definite (SPD) matrices they belong to. In the context of deep networks, several architectures for these matrices have recently been proposed. In our article, we introduce a Riemannian batch normalization (batchnorm) algorithm, which generalizes the one used in Euclidean nets. This novel layer makes use of geometric operations on the manifold, notably the Riemannian barycenter, parallel transport and non-linear structured matrix transformations. We derive a new manifold-constrained gradient descent algorithm working in the space of SPD matrices, allowing to learn the batchnorm layer. We validate our proposed approach with experiments in three different contexts on diverse data types: a drone recognition dataset from radar observations, and on emotion and action recognition datasets from video and motion capture data. Experiments show that the Riemannian batchnorm systematically gives better classification performance compared with leading methods and a remarkable robustness to lack of data.

Includes members:
  Member "a statistical recurrent model on the manifold of symmetric positive definite matrices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cpr gcn conditional partial residual graph convolutional network in automated anatomical labeling of coronary arteries")
  Member "manifoldnet a deep network framework for manifold valued data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "surreal complex valued deep learning as principled transformations on a rotational lie group")
  Member "learning a robust representation via a deep network on symmetric positive definite manifolds":
    also connected through other hyperedges:
      (none)
  Member "when kernel methods meet feature learning log covariance network for action recognition from skeletal data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ntu rgb d 120 a large scale benchmark for 3d human activity understanding")
  Member "deepkspd learning kernel matrix based spd representation for fine grained image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a neural network based on spd manifold learning for skeleton based hand gesture recognition")
  Member "towards faster training of global covariance pooling networks by iterative matrix square root normalization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "channel interaction networks for fine grained image categorization")
  Member "building deep networks on grassmann manifolds":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mvc net a convolutional neural network architecture for manifold valued images with applications")
  Member "deep learning on lie groups for skeleton based action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neural graph embedding methods for natural language processing")

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)