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 "learning invariant riemannian geometric representations using deep nets"):
  Abstract: Non-Euclidean constraints are inherent in many kinds of data in computer vision and machine learning, typically as a result of specific invariance requirements that need to be respected during high-level inference. Often, these geometric constraints can be expressed in the language of Riemannian geometry, where conventional vector space machine learning does not apply directly. The central question this paper deals with is: How does one train deep neural nets whose final outputs are elements on a Riemannian manifold? To answer this, we propose a general framework for manifold-aware training of deep neural networks -- we utilize tangent spaces and exponential maps in order to convert the proposed problem into a form that allows us to bring current advances in deep learning to bear upon this problem. We describe two specific applications to demonstrate this approach: prediction of probability distributions for multi-class image classification, and prediction of illumination-invariant subspaces from a single face-image via regression on the Grassmannian. These applications show the generality of the proposed framework, and result in improved performance over baselines that ignore the geometry of the output space. In addition to solving this specific problem, we believe this paper opens new lines of enquiry centered on the implications of Riemannian geometry on deep architectures.

Includes members:
  Member "end to end saliency mapping via probability distribution prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stavis spatio temporal audiovisual saliency network")
  Member "vinet visual inertial odometry as a sequence to sequence learning problem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "geometric deep learning going beyond euclidean data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "se3 nets learning rigid body motion using deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sequential forecasting of 100 000 points")
  Member "building deep networks on grassmann manifolds":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mvc net a convolutional neural network architecture for manifold valued images with applications")
  Member "generalized backpropagation etude de cas orthogonality":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "concept whitening for interpretable image recognition")
  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")
  Member "learning convolutional neural networks for graphs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")

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)