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: "context aware query image representation for particular object retrieval"
  Abstract: The current models of image representation based on Convolutional Neural Networks (CNN) have shown tremendous performance in image retrieval. Such models are inspired by the information flow along the visual pathway in the human visual cortex. We propose that in the field of particular object retrieval, the process of extracting CNN representations from query images with a given region of interest (ROI) can also be modelled by taking inspiration from human vision. Particularly, we show that by making the CNN pay attention on the ROI while extracting query image representation leads to significant improvement over the baseline methods on challenging Oxford5k and Paris6k datasets. Furthermore, we propose an extension to a recently introduced encoding method for CNN representations, regional maximum activations of convolutions (R-MAC). The proposed extension weights the regional representations using a novel saliency measure prior to aggregation. This leads to further improvement in retrieval accuracy.

Connected through hyperedge HE-1 (induced by source paper "geometric image correspondence verification by dense pixel matching"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "neighbourhood consensus networks"
    - "dgc net dense geometric correspondence network"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "convolutional neural network architecture for geometric matching"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "image patch matching using convolutional descriptors with euclidean distance"

Connected through hyperedge HE-2 (induced by source paper "afs an attention based mechanism for supervised feature selection"):
  with members:
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "attentional neural network feature selection using cognitive feedback"

Connected through hyperedge HE-3 (induced by source paper "an accurate retrieval through r mac descriptors for landmark recognition"):
  with members:
    - "class weighted convolutional features for visual instance search"
    - "a location aware embedding technique for accurate landmark recognition"
    - "saliency weighted convolutional features for instance search"
    - "end to end learning of deep visual representations for image retrieval"
    - "bags of local convolutional features for scalable instance search"
    - "deep image retrieval learning global representations for image search"
    - "sift meets cnn a decade survey of instance retrieval"
    - "cross dimensional weighting for aggregated deep convolutional features"

Connected through hyperedge HE-4 (induced by source paper "saliency weighted convolutional features for instance search"):
  with members:
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "unsupervised deep object discovery for instance recognition"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "end to end learning of deep visual representations for image retrieval"
    - "bags of local convolutional features for scalable instance search"
    - "shallow and deep convolutional networks for saliency prediction"
    - "efficient diffusion on region manifolds recovering small objects with compact cnn representations"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"

Connected through hyperedge HE-5 (induced by source paper "unsupervised deep object discovery for instance recognition"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "fast spectral ranking for similarity search"
    - "deep region hashing for efficient large scale instance search from images"
    - "faster r cnn features for instance search"
    - "end to end learning of deep visual representations for image retrieval"
    - "deep image retrieval learning global representations for image search"
    - "large scale image retrieval with attentive deep local features"
    - "efficient diffusion on region manifolds recovering small objects with compact cnn representations"

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)