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 "deep learning for 2d and 3d rotatable data an overview of methods"):
  Abstract: One of the reasons for the success of convolutional networks is their equivariance/invariance under translations. However, rotatable data such as molecules, living cells, everyday objects, or galaxies require processing with equivariance/invariance under rotations in cases where the rotation of the coordinate system does not affect the meaning of the data (e.g. object classification). On the other hand, estimation/processing of rotations is necessary in cases where rotations are important (e.g. motion estimation). There has been recent progress in methods and theory in all these regards. Here we provide an overview of existing methods, both for 2D and 3D rotations (and translations), and identify commonalities and links between them, in the hope that our insights will be useful for choosing and perfecting the methods.

Includes members:
  Member "n body networks a covariant hierarchical neural network architecture for learning atomic potentials":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on learning sets of symmetric elements")
  Member "rotdcf decomposition of convolutional filters for rotation equivariant deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "attentive group equivariant convolutional networks")
  Member "learning local rgb to cad correspondences for object pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "viewsynth learning local features from depth using view synthesis")
  Member "roto translation covariant convolutional networks for medical image analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "attentive group equivariant convolutional networks")
  Member "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a rotation invariant framework for deep point cloud analysis")
  Member "geonet unsupervised learning of dense depth optical flow and camera pose":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mini unmanned aerial vehicle based remote sensing techniques applications and prospects")
  Member "3d human pose estimation with siamese equivariant embedding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multiview consistent semi supervised learning for 3d human pose estimation")
  Member "cubenet equivariance to 3d rotation and translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on learning sets of symmetric elements")

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)