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 "surreal complex valued deep learning as principled transformations on a rotational lie group"):
  Abstract: Complex-valued deep learning has attracted increasing attention in recent years, due to its versatility and ability to capture more information. However, the lack of well-defined complex-valued operations remains a bottleneck for further advancement. In this work, we propose a geometric way to define deep neural networks on the space of complex numbers by utilizing weighted Frechet mean. We mathematically prove the viability of our algorithm. We also define basic building blocks such as convolution, non-linearity, and residual connections tailored for the space of complex numbers. To demonstrate the effectiveness of our proposed model, we compare our complex-valued network comprehensively with its real state-of-the-art counterpart on the MSTAR classification task and achieve better performance, while utilizing less than 1% of the parameters.

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 "dilated convolutional neural networks for sequential manifold valued data")
  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 "polar transformer networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "person re identification by contour sketch under moderate clothing change")
  Member "deep complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep quaternion features for privacy protection")
  Member "geometric deep learning going beyond euclidean data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "convolutional networks for spherical signals":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "powerset convolutional neural networks")
  Member "joint embedding and classification for sar target recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "surreal fr echet mean and distance transform for complex valued deep learning")

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)