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 "affine self convolution"):
  Abstract: Attention mechanisms, and most prominently self-attention, are a powerful building block for processing not only text but also images. These provide a parameter efficient method for aggregating inputs. We focus on self-attention in vision models, and we combine it with convolution, which as far as we know, are the first to do. What emerges is a convolution with data dependent filters. We call this an Affine Self Convolution. While this is applied differently at each spatial location, we show that it is translation equivariant. We also modify the Squeeze and Excitation variant of attention, extending both variants of attention to the roto-translation group. We evaluate these new models on CIFAR10 and CIFAR100 and show an improvement in the number of parameters, while reaching comparable or higher accuracy at test time against self-trained baselines.

Includes members:
  Member "deep scale spaces equivariance over scale":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "attentive group equivariant convolutional networks")
  Member "equivariant multi view networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "quaternion equivariant capsule networks for 3d point clouds")
  Member "stand alone self attention in vision models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "attentive group equivariant convolutional networks")
  Member "gauge equivariant convolutional networks and the icosahedral cnn":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on learning sets of symmetric elements")
  Member "attention augmented convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "attentive group equivariant convolutional networks")
  Member "local relation networks for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neuroevolution of self interpretable agents")
  Member "n body networks a covariant hierarchical neural network architecture for learning atomic potentials":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on learning sets of symmetric elements")
  Member "3d g cnns for pulmonary nodule detection":
    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.CV(Computer Vision and Pattern Recognition)