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 fr echet mean and distance transform for complex valued deep learning"):
  Abstract: We develop a novel deep learning architecture for naturally complex-valued data, which is often subject to complex scaling ambiguity. We treat each sample as a field in the space of complex numbers. With the polar form of a complex-valued number, the general group that acts in this space is the product of planar rotation and non-zero scaling. This perspective allows us to develop not only a novel convolution operator using weighted Frechet mean (wFM) on a Riemannian manifold, but also a novel fully connected layer operator using the distance to the wFM, with natural equivariant properties to non-zero scaling and planar rotation for the former and invariance properties for the latter. #R##N#Compared to the baseline approach of learning real-valued neural network models on the two-channel real-valued representation of complex-valued data, our method achieves surreal performance on two publicly available complex-valued datasets: MSTAR on SAR images and RadioML on radio frequency signals. On MSTAR, at 8% of the baseline model size and with fewer than 45,000 parameters, our model improves the target classification accuracy from 94% to 98% on this highly imbalanced dataset. On RadioML, our model achieves comparable RF modulation classification accuracy at 10% of the baseline model size.

Includes members:
  Member "manifoldnet a deep network framework for manifold valued data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "quaternion equivariant capsule networks for 3d point clouds")
  Member "polar transformer networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "deep quaternion features for privacy protection")
  Member "joint embedding and classification for sar target recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "surreal complex valued deep learning as principled transformations on a rotational lie group")
  Member "harmonic networks deep translation and rotation equivariance":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on learning sets of symmetric elements")
  Member "convolutional radio modulation recognition networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "group equivariant convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a permutation equivariant neural network architecture for auction design")

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)