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: "improving generalization via scalable neighborhood component analysis"
  Abstract: Current major approaches to visual recognition follow an end-to-end formulation that classifies an input image into one of the pre-determined set of semantic categories. Parametric softmax classifiers are a common choice for such a closed world with fixed categories, especially when big labeled data is available during training. However, this becomes problematic for open-set scenarios where new categories are encountered with very few examples for learning a generalizable parametric classifier. We adopt a non-parametric approach for visual recognition by optimizing feature embeddings instead of parametric classifiers. We use a deep neural network to learn the visual feature that preserves the neighborhood structure in the semantic space, based on the Neighborhood Component Analysis (NCA) criterion. Limited by its computational bottlenecks, we devise a mechanism to use augmented memory to scale NCA for large datasets and very deep networks. Our experiments deliver not only remarkable performance on ImageNet classification for such a simple non-parametric method, but most importantly a more generalizable feature representation for sub-category discovery and few-shot recognition.

Connected through hyperedge HE-1 (induced by source paper "learning to adapt invariance in memory for person re identification"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "joint discriminative and generative learning for person re identification"
    - "linkage based face clustering via graph convolution network"
    - "unsupervised tracklet person re identification"
    - "long term feature banks for detailed video understanding"
    - "dissecting person re identification from the viewpoint of viewpoint"
    - "harmonious attention network for person re identification"
    - "open set domain adaptation by backpropagation"

Connected through hyperedge HE-2 (induced by source paper "adaptive exploration for unsupervised person re identification"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "auto reid searching for a part aware convnet for person re identification"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "active multi kernel domain adaptation for hyperspectral image classification"
    - "self supervised video hashing with hierarchical binary auto encoder"
    - "dissecting person re identification from the viewpoint of viewpoint"
    - "multi task mid level feature alignment network for unsupervised cross dataset person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"

Connected through hyperedge HE-3 (induced by source paper "local aggregation for unsupervised learning of visual embeddings"):
  with members:
    - "contrastive multiview coding"
    - "data efficient image recognition with contrastive predictive coding"
    - "learning representations by maximizing mutual information across views"
    - "deep clustering for unsupervised learning of visual features"
    - "unsupervised representation learning by predicting image rotations"
    - "representation learning with contrastive predictive coding"
    - "self supervised feature learning by learning to spot artifacts"
    - "transitive invariance for self supervised visual representation learning"

Connected through hyperedge HE-4 (induced by source paper "instance cross entropy for deep metric learning"):
  with members:
    - "ranked list loss for deep metric learning"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "hardness aware deep metric learning"
    - "signal to noise ratio a robust distance metric for deep metric learning"
    - "divide and conquer the embedding space for metric learning"
    - "deep metric learning beyond binary supervision"
    - "decoupled networks"
    - "deep randomized ensembles for metric learning"

Connected through hyperedge HE-5 (induced by source paper "segsort segmentation by discriminative sorting of segments"):
  with members:
    - "adversarial structure matching for structured prediction tasks"
    - "deeperlab single shot image parser"
    - "efficient parameter free clustering using first neighbor relations"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning a discriminative feature network for semantic segmentation"
    - "cosface large margin cosine loss for deep face recognition"
    - "adaptive affinity fields for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-6 (induced by source paper "revisiting metric learning for few shot image classification"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "multi level semantic feature augmentation for one shot learning"
    - "meta learning with differentiable convex optimization"
    - "lgm net learning to generate matching networks for few shot learning"
    - "few shot learning with localization in realistic settings"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "edge labeling graph neural network for few shot learning"
    - "finding task relevant features for few shot learning by category traversal"

Connected through hyperedge HE-7 (induced by source paper "deep learning on small datasets without pre training using cosine loss"):
  with members:
    - "hierarchy based image embeddings for semantic image retrieval"
    - "tencent ml images a large scale multi label image database for visual representation learning"
    - "tafe net task aware feature embeddings for low shot learning"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "bag of tricks for image classification with convolutional neural networks"
    - "cosface large margin cosine loss for deep face recognition"
    - "dada deep adversarial data augmentation for extremely low data regime classification"
    - "directional statistics based deep metric learning for image classification and retrieval"

Connected through hyperedge HE-8 (induced by source paper "unsupervised learning from video with deep neural embeddings"):
  with members:
    - "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics"
    - "contrastive multiview coding"
    - "data efficient image recognition with contrastive predictive coding"
    - "local aggregation for unsupervised learning of visual embeddings"
    - "learning representations by maximizing mutual information across views"
    - "video representation learning by dense predictive coding"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "self supervised spatiotemporal feature learning via video rotation prediction"

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)