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: "sampling matters in deep embedding learning"
  Abstract: Deep embeddings answer one simple question: How similar are two images? Learning these embeddings is the bedrock of verification, zero-shot learning, and visual search. The most prominent approaches optimize a deep convolutional network with a suitable loss function, such as contrastive loss or triplet loss. While a rich line of work focuses solely on the loss functions, we show in this paper that selecting training examples plays an equally important role. We propose distance weighted sampling, which selects more informative and stable examples than traditional approaches. In addition, we show that a simple margin based loss is sufficient to outperform all other loss functions. We evaluate our approach on the Stanford Online Products, CAR196, and the CUB200-2011 datasets for image retrieval and clustering, and on the LFW dataset for face verification. Our method achieves state-of-the-art performance on all of them.

Connected through hyperedge HE-1 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning"):
  with members:
    - "ranked list loss for deep metric learning"
    - "metric learning with horde high order regularizer for deep embeddings"
    - "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"
    - "softtriple loss deep metric learning without triplet sampling"
    - "self supervised learning of pretext invariant representations"

Connected through hyperedge HE-2 (induced by source paper "magnifiernet towards semantic regularization and fusion for person re identification"):
  with members:
    - "interaction and aggregation network for person re identification"
    - "beyond human parts dual part aligned representations for person re identification"
    - "aanet attribute attention network for person re identifications"
    - "joint discriminative and generative learning for person re identification"
    - "omni scale feature learning for person re identification"
    - "foreground aware pyramid reconstruction for alignment free occluded person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "improving person re identification by attribute and identity learning"

Connected through hyperedge HE-3 (induced by source paper "a metric learning reality check"):
  with members:
    - "a simple framework for contrastive learning of visual representations"
    - "revisiting training strategies and generalization performance in deep metric learning"
    - "unbiased evaluation of deep metric learning algorithms"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "hardness aware deep metric learning"
    - "momentum contrast for unsupervised visual representation learning"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "signal to noise ratio a robust distance metric for deep metric learning"

Connected through hyperedge HE-4 (induced by source paper "circle loss a unified perspective of pair similarity optimization"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "adacos adaptively scaling cosine logits for effectively learning deep face representations"
    - "softmax dissection towards understanding intra and inter class objective for embedding learning"
    - "learning discriminative features with multiple granularities for person re identification"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-5 (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-6 (induced by source paper "featurenms non maximum suppression by learning feature embeddings"):
  with members:
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "large batch optimization for deep learning training bert in 76 minutes"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "yolov3 an incremental improvement"
    - "crowdhuman a benchmark for detecting human in a crowd"
    - "deep learning based large scale visual recommendation and search for e commerce"
    - "soft nms improving object detection with one line of code"
    - "focal loss for dense object detection"

Connected through hyperedge HE-7 (induced by source paper "informative sample mining network for multi domain image to image translation"):
  with members:
    - "hardness aware deep metric learning"
    - "stgan a unified selective transfer network for arbitrary image attribute editing"
    - "relgan multi domain image to image translation via relative attributes"
    - "hard aware point to set deep metric for person re identification"
    - "attgan facial attribute editing by only changing what you want"
    - "progressive growing of gans for improved quality stability and variation"
    - "deep metric learning with angular loss"
    - "unsupervised image to image translation networks"

Connected through hyperedge HE-8 (induced by source paper "layer wise pruning and auto tuning of layer wise learning rates in fine tuning of deep networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "backbone can not be trained at once rolling back to pre trained network for person re identification"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "do better imagenet models transfer better"
    - "classification is a strong baseline for deep metric learning"
    - "learning to navigate for fine grained classification"
    - "deep randomized ensembles for metric learning"

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)