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: "generalized orderless pooling performs implicit salient matching"
  Abstract: Most recent CNN architectures use average pooling as a final feature encoding step. In the field of fine-grained recognition, however, recent global representations like bilinear pooling offer improved performance. In this paper, we generalize average and bilinear pooling to "alpha-pooling", allowing for learning the pooling strategy during training. In addition, we present a novel way to visualize decisions made by these approaches. We identify parts of training images having the highest influence on the prediction of a given test image. It allows for justifying decisions to users and also for analyzing the influence of semantic parts. For example, we can show that the higher capacity VGG16 model focuses much more on the bird's head than, e.g., the lower-capacity VGG-M model when recognizing fine-grained bird categories. Both contributions allow us to analyze the difference when moving between average and bilinear pooling. In addition, experiments show that our generalized approach can outperform both across a variety of standard datasets.

Connected through hyperedge HE-1 (induced by source paper "cis net a novel cnn model for spatial image steganalysis via cover image suppression"):
  with members:
    - "deep steganalysis end to end learning with supervisory information beyond class labels"
    - "large scale jpeg image steganalysis using hybrid deep learning framework"
    - "deep convolutional neural network to detect j uniward"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "a novel convolutional neural network for image steganalysis with shared normalization"
    - "improved relation classification by deep recurrent neural networks with data augmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "large scale fine grained categorization and domain specific transfer learning"):
  with members:
    - "teaching categories to human learners with visual explanations"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "the devil is in the tails fine grained classification in the wild"
    - "learning transferable architectures for scalable image recognition"
    - "deep layer aggregation"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "squeeze and excitation networks"
    - "improved bilinear pooling with cnns"

Connected through hyperedge HE-3 (induced by source paper "multi attention multi class constraint for fine grained image recognition"):
  with members:
    - "deep metric learning with angular loss"
    - "squeeze and excitation networks"
    - "diversified visual attention networks for fine grained object classification"
    - "residual attention network for image classification"
    - "the inaturalist species classification and detection dataset"
    - "dynamic computational time for visual attention"
    - "soft proposal networks for weakly supervised object localization"
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"

Connected through hyperedge HE-4 (induced by source paper "question type guided attention in visual question answering"):
  with members:
    - "vizwiz grand challenge answering visual questions from blind people"
    - "structured triplet learning with pos tag guided attention for visual question answering"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "an analysis of visual question answering algorithms"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "end to end optimization of goal driven and visually grounded dialogue systems"
    - "training recurrent answering units with joint loss minimization for vqa"

Connected through hyperedge HE-5 (induced by source paper "local temporal bilinear pooling for fine grained action parsing"):
  with members:
    - "learning motion in feature space locally consistent deformable convolution networks for fine grained action detection"
    - "compact generalized non local network"
    - "temporal human action segmentation via dynamic clustering"
    - "monet moments embedding network"
    - "hierarchical bilinear pooling for fine grained visual recognition"
    - "a deeper look at power normalizations"
    - "second order democratic aggregation"
    - "statistically motivated second order pooling"

Connected through hyperedge HE-6 (induced by source paper "impostor networks for fast fine grained recognition"):
  with members:
    - "nearest neighbour radial basis function solvers for deep neural networks"
    - "billion scale similarity search with gpus"
    - "no fuss distance metric learning using proxies"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "the inaturalist species classification and detection dataset"
    - "lcnn lookup based convolutional neural network"
    - "learning structured sparsity in deep neural networks"
    - "deep image retrieval learning global representations for image search"

Connected through hyperedge HE-7 (induced by source paper "maximum entropy fine grained classification"):
  with members:
    - "improved bilinear pooling with cnns"
    - "regularizing neural networks by penalizing confident output distributions"
    - "learning online alignments with continuous rewards policy gradient"
    - "asynchronous methods for deep reinforcement learning"
    - "compact bilinear pooling"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "alpha integration pooling for convolutional neural networks"):
  with members:
    - "aggregated residual transformations for deep neural networks"
    - "compact bilinear pooling"
    - "very deep convolutional networks for large scale image recognition"
    - "learned norm pooling for deep feedforward and recurrent neural networks"
    - "stochastic pooling for regularization of deep convolutional neural networks"

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)