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 "dilated convolutional neural networks for sequential manifold valued data"):
  Abstract: Efforts are underway to study ways via which the power of deep neural networks can be extended to non-standard data types such as structured data (e.g., graphs) or manifold-valued data (e.g., unit vectors or special matrices). Often, sizable empirical improvements are possible when the geometry of such data spaces are incorporated into the design of the model, architecture, and the algorithms. Motivated by neuroimaging applications, we study formulations where the data are {\em sequential manifold-valued measurements}. This case is common in brain imaging, where the samples correspond to symmetric positive definite matrices or orientation distribution functions. Instead of a recurrent model which poses computational/technical issues, and inspired by recent results showing the viability of dilated convolutional models for sequence prediction, we develop a dilated convolutional neural network architecture for this task. On the technical side, we show how the modules needed in our network can be derived while explicitly taking the Riemannian manifold structure into account. We show how the operations needed can leverage known results for calculating the weighted Frechet Mean (wFM). Finally, we present scientific results for group difference analysis in Alzheimer's disease (AD) where the groups are derived using AD pathology load: here the model finds several brain fiber bundles that are related to AD even when the subjects are all still cognitively healthy.

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 "h cnns convolutional neural networks for riemannian homogeneous spaces":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "quaternion equivariant capsule networks for 3d point clouds")
  Member "covariant compositional networks for learning graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "the statistical recurrent unit":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards non saturating recurrent units for modelling long term dependencies")
  Member "tensor train recurrent neural networks for video classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "convolutional tensor train lstm for spatio temporal learning")
  Member "geometric deep learning going beyond euclidean data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "second order convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mvc net a convolutional neural network architecture for manifold valued images with applications")

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)