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: "hardness aware deep metric learning"
  Abstract: This paper presents a hardness-aware deep metric learning (HDML) framework. Most previous deep metric learning methods employ the hard negative mining strategy to alleviate the lack of informative samples for training. However, this mining strategy only utilizes a subset of training data, which may not be enough to characterize the global geometry of the embedding space comprehensively. To address this problem, we perform linear interpolation on embeddings to adaptively manipulate their hard levels and generate corresponding label-preserving synthetics for recycled training, so that information buried in all samples can be fully exploited and the metric is always challenged with proper difficulty. Our method achieves very competitive performance on the widely used CUB-200-2011, Cars196, and Stanford Online Products datasets.

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"
    - "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"
    - "deep metric learning with bier boosting independent embeddings robustly"

Connected through hyperedge HE-2 (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"
    - "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"
    - "softtriple loss deep metric learning without triplet sampling"

Connected through hyperedge HE-3 (induced by source paper "adapting grad cam for embedding networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "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"
    - "grounding visual explanations"

Connected through hyperedge HE-4 (induced by source paper "informative sample mining network for multi domain image to image translation"):
  with members:
    - "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"
    - "sampling matters in deep embedding learning"
    - "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-5 (induced by source paper "symmetrical synthesis for deep metric learning"):
  with members:
    - "hard aware point to set deep metric for person re identification"
    - "sampling matters in deep embedding learning"
    - "in defense of the triplet loss for person re identification"
    - "deep metric learning with angular loss"
    - "fine grained categorization and dataset bootstrapping using deep metric learning with humans in the loop"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "facenet a unified embedding for face recognition and clustering"
    - "deep metric learning via lifted structured feature embedding"

Connected through hyperedge HE-6 (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"
    - "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"
    - "learning towards minimum hyperspherical energy"

Connected through hyperedge HE-7 (induced by source paper "metric learning for adversarial robustness"):
  with members:
    - "are labels required for improving adversarial robustness"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "theoretically principled trade off between robustness and accuracy"
    - "an analysis of the t sne algorithm for data visualization"
    - "formal security analysis of neural networks using symbolic intervals"
    - "logit pairing methods can fool gradient based attacks"
    - "feature denoising for improving adversarial robustness"
    - "adversarial logit pairing"

Connected through hyperedge HE-8 (induced by source paper "unsupervised deep metric learning via auxiliary rotation loss"):
  with members:
    - "on mutual information maximization for representation learning"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "unsupervised pre training of image features on non curated data"
    - "data efficient image recognition with contrastive predictive coding"
    - "learning representations by maximizing mutual information across views"
    - "unsupervised embedding learning via invariant and spreading instance feature"
    - "grasp2vec learning object representations from self supervised grasping"

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)