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: "learning what and where to attend"
  Abstract: Most recent gains in visual recognition have originated from the inclusion of attention mechanisms in deep convolutional networks (DCNs). Because these networks are optimized for object recognition, they learn where to attend using only a weak form of supervision derived from image class labels. Here, we demonstrate the benefit of using stronger supervisory signals by teaching DCNs to attend to image regions that humans deem important for object recognition. We first describe a large-scale online experiment (ClickMe) used to supplement ImageNet with nearly half a million human-derived "top-down" attention maps. Using human psychophysics, we confirm that the identified top-down features from ClickMe are more diagnostic than "bottom-up" saliency features for rapid image categorization. As a proof of concept, we extend a state-of-the-art attention network and demonstrate that adding ClickMe supervision significantly improves its accuracy and yields visual features that are more interpretable and more similar to those used by human observers.

Connected through hyperedge HE-1 (induced by source paper "stochastic region pooling make attention more expressive"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multi attention multi class constraint for fine grained image recognition"
    - "harmonious attention network for person re identification"
    - "dropblock a regularization method for convolutional networks"
    - "convolutional neural networks with alternately updated clique"
    - "competitive inner imaging squeeze and excitation for residual network"
    - "gather excite exploiting feature context in convolutional neural networks"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-2 (induced by source paper "putting visual object recognition in context"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "lift the flap context reasoning using object centered graphs"
    - "iterative visual reasoning beyond convolutions"
    - "yolov3 an incremental improvement"
    - "modeling visual context is key to augmenting object detection datasets"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "learning scene gist with convolutional neural networks to improve object recognition"
    - "structure inference net object detection using scene level context and instance level relationships"

Connected through hyperedge HE-3 (induced by source paper "fibinet combining feature importance and bilinear feature interaction for click through rate prediction"):
  with members:
    - "skin lesion classification with ensemble of squeeze and excitation networks and semi supervised learning"
    - "weakly supervised deep learning for thoracic disease classification and localization on chest x rays"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "recalibrating fully convolutional networks with spatial and channel squeeze excitation blocks"
    - "neural factorization machines for sparse predictive analytics"
    - "field aware factorization machines in a real world online advertising system"
    - "deep cross network for ad click predictions"
    - "squeeze and excitation networks"

Connected through hyperedge HE-4 (induced by source paper "pose guided attention for multi label fashion image classification"):
  with members:
    - "cbam convolutional block attention module"
    - "understanding traffic density from large scale web camera data"
    - "focal loss for dense object detection"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "competitive inner imaging squeeze and excitation for residual network"):
  with members:
    - "multi attention multi class constraint for fine grained image recognition"
    - "convolutional neural networks with alternately updated clique"
    - "connectivity learning in multi branch networks"
    - "neural ordinary differential equations"
    - "cbam convolutional block attention module"
    - "skipnet learning dynamic routing in convolutional networks"
    - "learning transferable architectures for scalable image recognition"
    - "interleaved group convolutions for deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "robust neural circuit reconstruction from serial electron microscopy with convolutional recurrent networks"):
  with members:
    - "bi directional cascade network for perceptual edge detection"
    - "deep predictive coding network for object recognition"
    - "recurrent residual convolutional neural network based on u net r2u net for medical image segmentation"
    - "deep predictive coding network with local recurrent processing for object recognition"
    - "learning long range spatial dependencies with horizontal gated recurrent units"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "can recurrent neural networks warp time"
    - "the elephant in the room"

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)