human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "equivariant transformer networks"
  Abstract: How can prior knowledge on the transformation invariances of a domain be incorporated into the architecture of a neural network? We propose Equivariant Transformers (ETs), a family of differentiable image-to-image mappings that improve the robustness of models towards pre-defined continuous transformation groups. Through the use of specially-derived canonical coordinate systems, ETs incorporate functions that are equivariant by construction with respect to these transformations. We show empirically that ETs can be flexibly composed to improve model robustness towards more complicated transformation groups in several parameters. On a real-world image classification task, ETs improve the sample efficiency of ResNet classifiers, achieving relative improvements in error rate of up to 15% in the limited data regime while increasing model parameter count by less than 1%.

Connected through hyperedge HE-1 (induced by source paper "invariance inducing regularization using worst case transformations suffices to boost accuracy and spatial robustness"):
  with members:
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training can hurt generalization"
    - "unsupervised data augmentation for consistency training"
    - "adversarial logit pairing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "generalisation in humans and deep neural networks"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "scale equivariant steerable networks"):
  with members:
    - "deep scale spaces equivariance over scale"
    - "scale steerable filters for locally scale invariant convolutional neural networks"
    - "harmonic networks with limited training samples"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "cubenet equivariance to 3d rotation and translation"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"
    - "scale equivariance in cnns with vector fields"

Connected through hyperedge HE-3 (induced by source paper "gift learning transformation invariant dense visual descriptors via group cnns"):
  with members:
    - "equivariant multi view networks"
    - "an improved learning framework for covariant local feature detection"
    - "local descriptors optimized for average precision"
    - "large scale evaluation of local image feature detectors on homography datasets"
    - "superpoint self supervised interest point detection and description"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "graph based isometry invariant representation learning"
    - "instance normalization the missing ingredient for fast stylization"

Connected through hyperedge HE-4 (induced by source paper "building deep equivariant capsule networks"):
  with members:
    - "deepcaps going deeper with capsule networks"
    - "attention routing between capsules"
    - "the multi lane capsule network mlcn"
    - "context aware multipath networks"
    - "deep learning for classical japanese literature"
    - "group equivariant capsule networks"
    - "brain tumor type classification via capsule networks"
    - "hitnet a neural network with capsules embedded in a hit or miss layer extended with hybrid data augmentation and ghost capsules"

Question: Please tell me which class the center node 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)