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 "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.

Includes members:
  Member "very deep self attention networks for end to end speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improving uyghur asr systems with decoders using morpheme based language models")
  Member "connnet a long range relation aware pixel connectivity network for salient segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "context integrated and feature refined network for lightweight object parsing")
  Member "xlnet generalized autoregressive pretraining for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on contextual embeddings")
  Member "learning depth with convolutional spatial propagation network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "belief propagation reloaded learning bp layers for labeling problems")
  Member "yolov3 an incremental improvement":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "video based person re identification via 3d convolutional networks and non local attention":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "interlaced sparse self attention for semantic segmentation")
  Member "non locally enhanced encoder decoder network for single image de raining":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gated fusion network for degraded image super resolution")
  Member "non local recurrent network for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fine grained image to image transformation towards visual recognition")

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)