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: "generalization in metric learning should the embedding layer be the embedding layer"
  Abstract: Many recent works advancing deep learning tend to focus on large scale setting with the goal of more effective training and better fitting. This goal might be less applicable to the case of small to medium scale. Studying deep metric learning under such setting, we reason that better generalization could be a big contributing factor to improvement of previous works, as well as the goal for further improvement. #R##N#We investigate using other layers in a deep metric learning system (beside the embedding layer) for feature extraction and analyze how well they perform on training data and generalize to testing data. From this study, we suggest a new regularization practice and demonstrate state-of-the-art performance on 3 fine-grained image retrieval benchmarks: Cars-196, CUB-200-2011 and Stanford Online Product.

Connected through hyperedge HE-1 (induced by source paper "adapting grad cam for embedding networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "hardness aware deep metric learning"
    - "visualizing how embeddings generalize"
    - "improved embeddings with easy positive triplet mining"
    - "divide and conquer the embedding space for metric learning"
    - "visual explanation for deep metric learning"
    - "visualizing deep similarity networks"
    - "deep metric learning beyond binary supervision"

Connected through hyperedge HE-2 (induced by source paper "semantic granularity metric learning for visual search"):
  with members:
    - "deep randomized ensembles for metric learning"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "deep metric learning with hierarchical triplet loss"
    - "deep learning based large scale visual recommendation and search for e commerce"
    - "in defense of the triplet loss for person re identification"
    - "local similarity aware deep feature embedding"
    - "hard aware deeply cascaded embedding"
    - "simultaneous feature learning and hash coding with deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "let s transfer transformations of shared semantic representations"):
  with members:
    - "composing text and image for image retrieval an empirical odyssey"
    - "text2shape generating shapes from natural language by learning joint embeddings"
    - "image generation from scene graphs"
    - "dialog based interactive image retrieval"
    - "texturegan controlling deep image synthesis with texture patches"
    - "automatic spatially aware fashion concept discovery"
    - "adversarial discriminative domain adaptation"
    - "scene graph generation by iterative message passing"

Connected through hyperedge HE-4 (induced by source paper "visualizing deep similarity networks"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "in defense of the triplet loss for person re identification"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "no fuss distance metric learning using proxies"
    - "learning deep embeddings with histogram loss"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-5 (induced by source paper "hierarchical clustering guided re id with triplet loss"):
  with members:
    - "towards better validity dispersion based clustering for unsupervised person re identification"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "unsupervised domain adaptive re identification theory and practice"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "image image domain adaptation with preserved self similarity and domain dissimilarity for person re identification"

Connected through hyperedge HE-6 (induced by source paper "visual search over billions of aerial and satellite images"):
  with members:
    - "visual discovery at pinterest"
    - "good practice in cnn feature transfer"
    - "sift meets cnn a decade survey of instance retrieval"
    - "data intensive supercomputing in the cloud global analytics for satellite imagery"
    - "deep residual learning for image recognition"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "visual search at pinterest"
    - "neural codes for image retrieval"

Connected through hyperedge HE-7 (induced by source paper "unsupervised domain adaptive re identification theory and practice"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "adaptation and re identification network an unsupervised deep transfer learning approach to person re identification"
    - "image image domain adaptation with preserved self similarity and domain dissimilarity for person re identification"
    - "re ranking person re identification with k reciprocal encoding"
    - "dynamic label graph matching for unsupervised video re identification"
    - "unsupervised person re identification clustering and fine tuning"
    - "performance measures and a data set for multi target multi camera tracking"

Connected through hyperedge HE-8 (induced by source paper "learning embeddings for product visual search with triplet loss and online sampling"):
  with members:
    - "attention based ensemble for deep metric learning"
    - "identity mappings in deep residual networks"
    - "facenet a unified embedding for face recognition and clustering"
    - "deep metric learning via lifted structured feature embedding"
    - "imagenet large scale visual recognition challenge"
    - "neural codes for image retrieval"

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)