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: "cross convolutional layer pooling for image recognition"
  Abstract: Recent studies have shown that a Deep Convolutional Neural Network (DCNN) pretrained on a large image dataset can be used as a universal image descriptor, and that doing so leads to impressive performance for a variety of image classification tasks. Most of these studies adopt activations from a single DCNN layer, usually the fully-connected layer, as the image representation. In this paper, we proposed a novel way to extract image representations from two consecutive convolutional layers: one layer is utilized for local feature extraction and the other serves as guidance to pool the extracted features. By taking different viewpoints of convolutional layers, we further develop two schemes to realize this idea. The first one directly uses convolutional layers from a DCNN. The second one applies the pretrained CNN on densely sampled image regions and treats the fully-connected activations of each image region as convolutional feature activations. We then train another convolutional layer on top of that as the pooling-guidance convolutional layer. By applying our method to three popular visual classification tasks, we find our first scheme tends to perform better on the applications which need strong discrimination on subtle object patterns within small regions while the latter excels in the cases that require discrimination on category-level patterns. Overall, the proposed method achieves superior performance over existing ways of extracting image representations from a DCNN.

Connected through hyperedge HE-1 (induced by source paper "camal context aware multi scale attention framework for lightweight visual place recognition"):
  with members:
    - "levelling the playing field a comprehensive comparison of visual place recognition approaches under changing conditions"
    - "condition invariant multi view place recognition"
    - "a holistic visual place recognition approach using lightweight cnns for severe viewpoint and appearance changes"
    - "deep learning features at scale for visual place recognition"
    - "multi context attention for human pose estimation"
    - "residual attention network for image classification"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "visual place recognition using landmark distribution descriptors"

Connected through hyperedge HE-2 (induced by source paper "coarse to fine a rnn based hierarchical attention model for vehicle re identification"):
  with members:
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "ssd single shot multibox detector"
    - "phi lstm a phrase based hierarchical lstm model for image captioning"
    - "ask me anything free form visual question answering based on knowledge from external sources"
    - "a large scale car dataset for fine grained categorization and verification"
    - "what value do explicit high level concepts have in vision to language problems"
    - "deep feature learning with relative distance comparison for person re identification"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-3 (induced by source paper "a holistic visual place recognition approach using lightweight cnns for severe viewpoint and appearance changes"):
  with members:
    - "from selective deep convolutional features to compact binary representations for image retrieval"
    - "detect to retrieve efficient regional aggregation for image search"
    - "deep learning features at scale for visual place recognition"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "sift meets cnn a decade survey of instance retrieval"
    - "visual place recognition using landmark distribution descriptors"
    - "spatial transformer networks"
    - "particular object retrieval with integral max pooling of cnn activations"

Connected through hyperedge HE-4 (induced by source paper "learning a robust representation via a deep network on symmetric positive definite manifolds"):
  with members:
    - "deep manifold to manifold transforming network for action recognition"
    - "deepkspd learning kernel matrix based spd representation for fine grained image recognition"
    - "is second order information helpful for large scale visual recognition"
    - "second order convolutional neural networks"
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"
    - "exploiting structure sparsity for covariance based visual representation"
    - "deep ten texture encoding network"
    - "learning an invariant hilbert space for domain adaptation"

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)