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: "efficient attention attention with linear complexities"
  Abstract: The attention mechanism has seen wide applications in computer vision and natural language processing. Recent works developed the dot-product attention mechanism and applied it to various vision and language tasks. However, the memory and computational costs of dot-product attention grows quadratically with the spatiotemporal size of the input. Such growth prohibits the application of the mechanism on large inputs, e.g., long sequences, high-resolution images, or large videos. To remedy this drawback, this paper proposes a novel efficient attention mechanism, which is equivalent to dot-product attention but has substantially less memory and computational costs. The resource efficiency allows more widespread and flexible incorporation of efficient attention modules into a neural network, which leads to improved accuracies. Empirical evaluations on object recognition and image classification demonstrated the effectiveness of its advantages. Models with efficient attention achieved state-of-the-art performance on MS-COCO 2017 and significant improvement on ImageNet. Further, the resource efficiency of the mechanism democratizes attention to complicated models, which were unable to incorporate original dot-product attention due to prohibitively high costs. As an exemplar, an efficient attention-augmented model achieved state-of-the-art accuracies for stereo depth estimation on the Scene Flow dataset. Code is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "interlaced sparse self attention for semantic segmentation"):
  with members:
    - "deeperlab single shot image parser"
    - "generating long sequences with sparse transformers"
    - "semantic correlation promoted shape variant context for segmentation"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "devil in the details towards accurate single and multiple human parsing"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"

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)