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: "a practical guide to cnns and fisher vectors for image instance retrieval"
  Abstract: With deep learning becoming the dominant approach in computer vision, the use of representations extracted from Convolutional Neural Nets (CNNs) is quickly gaining ground on Fisher Vectors (FVs) as favoured state-of-the-art global image descriptors for image instance retrieval. While the good performance of CNNs for image classification are unambiguously recognised, which of the two has the upper hand in the image retrieval context is not entirely clear yet. In this work, we propose a comprehensive study that systematically evaluates FVs and CNNs for image retrieval. The first part compares the performances of FVs and CNNs on multiple publicly available data sets. We investigate a number of details specific to each method. For FVs, we compare sparse descriptors based on interest point detectors with dense single-scale and multi-scale variants. For CNNs, we focus on understanding the impact of depth, architecture and training data on retrieval results. Our study shows that no descriptor is systematically better than the other and that performance gains can usually be obtained by using both types together. The second part of the study focuses on the impact of geometrical transformations such as rotations and scale changes. FVs based on interest point detectors are intrinsically resilient to such transformations while CNNs do not have a built-in mechanism to ensure such invariance. We show that performance of CNNs can quickly degrade in presence of rotations while they are far less affected by changes in scale. We then propose a number of ways to incorporate the required invariances in the CNN pipeline. Overall, our work is intended as a reference guide offering practically useful and simply implementable guidelines to anyone looking for state-of-the-art global descriptors best suited to their specific image instance retrieval problem.

Connected through hyperedge HE-1 (induced by source paper "a comprehensive analysis of deep regression"):
  with members:
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "lstm a search space odyssey"
    - "learning feature pyramids for human pose estimation"
    - "viraliency pooling local virality"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "robust head pose estimation based on partially latent mixture of linear regressions"
    - "deep convolutional neural network design patterns"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"

Connected through hyperedge HE-2 (induced by source paper "patternnet a benchmark dataset for performance evaluation of remote sensing image retrieval"):
  with members:
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "learning low dimensional convolutional neural networks for high resolution remote sensing image retrieval"
    - "remote sensing image scene classification benchmark and state of the art"
    - "selective convolutional descriptor aggregation for fine grained image retrieval"
    - "deep image retrieval learning global representations for image search"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"
    - "visual descriptors for content based retrieval of remote sensing images"

Connected through hyperedge HE-3 (induced by source paper "aggregating binary local descriptors for image retrieval"):
  with members:
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "latch learned arrangements of three patch codes"
    - "exploiting local features from deep networks for image retrieval"
    - "very deep convolutional networks for large scale image recognition"
    - "orientation covariant aggregation of local descriptors with embeddings"
    - "caffe convolutional architecture for fast feature embedding"
    - "neural codes for image retrieval"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-4 (induced by source paper "hybrid feature learning for handwriting verification"):
  with members:
    - "signet convolutional siamese network for writer independent offline signature verification"
    - "sift meets cnn a decade survey of instance retrieval"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "exploiting deep features for remote sensing image retrieval a systematic investigation"):
  with members:
    - "patternnet a benchmark dataset for performance evaluation of remote sensing image retrieval"
    - "texture characterization using shape co occurrence patterns"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "learning low dimensional convolutional neural networks for high resolution remote sensing image retrieval"
    - "deep image retrieval learning global representations for image search"
    - "visual descriptors for content based retrieval of remote sensing images"
    - "distributed mining of large scale remote sensing image archives on public computing infrastructures"
    - "cross dimensional weighting for aggregated deep convolutional features"

Connected through hyperedge HE-6 (induced by source paper "compression of deep neural networks for image instance retrieval"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "nested invariance pooling and rbm hashing for image instance retrieval"
    - "group invariant deep representations for image instance retrieval"
    - "bridging the gaps between residual learning recurrent neural networks and visual cortex"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "deep residual learning for image recognition"
    - "particular object retrieval with integral max pooling of cnn activations"

Connected through hyperedge HE-7 (induced by source paper "why my photos look sideways or upside down detecting canonical orientation of images using convolutional neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "places an image database for deep scene understanding"
    - "compensating for large in plane rotations in natural images"
    - "spatial transformer networks"
    - "how transferable are features in deep neural networks"
    - "object detectors emerge in deep scene cnns"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-8 (induced by source paper "learning low dimensional convolutional neural networks for high resolution remote sensing image retrieval"):
  with members:
    - "visual descriptors for content based retrieval of remote sensing images"
    - "exploiting local features from deep networks for image retrieval"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "caffe convolutional architecture for fast feature embedding"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "network in network"
    - "improving neural networks by preventing co adaptation of feature detectors"

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)