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: "sift meets cnn a decade survey of instance retrieval"
  Abstract: In the early days, content-based image retrieval (CBIR) was studied with global features. Since 2003, image retrieval based on local descriptors (de facto SIFT) has been extensively studied for over a decade due to the advantage of SIFT in dealing with image transformations. Recently, image representations based on the convolutional neural network (CNN) have attracted increasing interest in the community and demonstrated impressive performance. Given this time of rapid evolution, this article provides a comprehensive survey of instance retrieval over the last decade. Two broad categories, SIFT-based and CNN-based methods, are presented. For the former, according to the codebook size, we organize the literature into using large/medium-sized/small codebooks. For the latter, we discuss three lines of methods, i.e., using pre-trained or fine-tuned CNN models, and hybrid methods. The first two perform a single-pass of an image to the network, while the last category employs a patch-based feature extraction scheme. This survey presents milestones in modern instance retrieval, reviews a broad selection of previous works in different categories, and provides insights on the connection between SIFT and CNN-based methods. After analyzing and comparing retrieval performance of different categories on several datasets, we discuss promising directions towards generic and specialized instance retrieval.

Connected through hyperedge HE-1 (induced by source paper "matching neuromorphic events and color images via adversarial learning"):
  with members:
    - "event based vision a survey"
    - "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"
    - "rift multi modal image matching based on radiation invariant feature transform"
    - "event based vision meets deep learning on steering prediction for self driving cars"
    - "converting static image datasets to spiking neuromorphic datasets using saccades"
    - "deep residual learning for image recognition"
    - "sequence to sequence video to text"
    - "understanding deep image representations by inverting them"

Connected through hyperedge HE-2 (induced by source paper "an analysis of object embeddings for image retrieval"):
  with members:
    - "learning rich features for image manipulation detection"
    - "do better imagenet models transfer better"
    - "deep learning for generic object detection a survey"
    - "dgc net dense geometric correspondence network"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "the ps battles dataset an image collection for image manipulation detection"
    - "sniper efficient multi scale training"
    - "deep metric learning with hierarchical triplet loss"

Connected through hyperedge HE-3 (induced by source paper "few example object detection with model communication"):
  with members:
    - "zero shot detection"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "active self paced learning for cost effective and progressive face identification"
    - "collaborative learning for weakly supervised object detection"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "bridging saliency detection to weakly supervised object detection based on self paced curriculum learning"
    - "learning to segment human by watching youtube"

Connected through hyperedge HE-4 (induced by source paper "unsupervised semantic based aggregation of deep convolutional features"):
  with members:
    - "a discriminatively learned cnn embedding for person reidentification"
    - "unsupervised part based weighting aggregation of deep convolutional features for image retrieval"
    - "iterative manifold embedding layer learned by incomplete data for large scale image retrieval"
    - "fine tuning cnn image retrieval with no human annotation"
    - "residual attention network for image classification"
    - "mask r cnn"
    - "selective convolutional descriptor aggregation for fine grained image retrieval"
    - "end to end learning of deep visual representations for image retrieval"

Connected through hyperedge HE-5 (induced by source paper "deep semantic multimodal hashing network for scalable multimedia retrieval"):
  with members:
    - "deep ordinal hashing with spatial attention"
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "deep supervised discrete hashing"
    - "part based deep hashing for large scale person re identification"
    - "mask r cnn"
    - "object detection from video tubelets with convolutional neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"

Connected through hyperedge HE-6 (induced by source paper "person re identification by semantic region representation and topology constraint"):
  with members:
    - "harmonious attention network for person re identification"
    - "in defense of the triplet loss for person re identification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "camera style adaptation for person re identification"
    - "deep hybrid similarity learning for person re identification"
    - "part based deep hashing for large scale person re identification"
    - "pose driven deep convolutional model for person re identification"

Connected through hyperedge HE-7 (induced by source paper "consistent cross view matching for unsupervised person re identification"):
  with members:
    - "unsupervised person re identification by soft multilabel learning"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "multi level factorisation net for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "deep association learning for unsupervised video person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "part aligned bilinear representations for person re identification"

Connected through hyperedge HE-8 (induced by source paper "person re identification with metric learning using privileged information"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "re ranking person re identification with k reciprocal encoding"
    - "enhancing person re identification in a self trained subspace"
    - "end to end comparative attention networks for person re identification"
    - "transferring a semantic representation for person re identification and search"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "learning a discriminative null space for person re identification"
    - "temporal model adaptation for person re identification"

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)