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: "multi similarity loss with general pair weighting for deep metric learning"
  Abstract: A family of loss functions built on pair-based computation have been proposed in the literature which provide a myriad of solutions for deep metric learning. In this paper, we provide a general weighting framework for understanding recent pair-based loss functions. Our contributions are three-fold: (1) we establish a General Pair Weighting (GPW) framework, which casts the sampling problem of deep metric learning into a unified view of pair weighting through gradient analysis, providing a powerful tool for understanding recent pair-based loss functions; (2) we show that with GPW, various existing pair-based methods can be compared and discussed comprehensively, with clear differences and key limitations identified; (3) we propose a new loss called multi-similarity loss (MS loss) under the GPW, which is implemented in two iterative steps (i.e., mining and weighting). This allows it to fully consider three similarities for pair weighting, providing a more principled approach for collecting and weighting informative pairs. Finally, the proposed MS loss obtains new state-of-the-art performance on four image retrieval benchmarks, where it outperforms the most recent approaches, such as ABE\cite{Kim_2018_ECCV} and HTL by a large margin: 60.6% to 65.7% on CUB200, and 80.9% to 88.0% on In-Shop Clothes Retrieval dataset at Recall@1. Code is available at this https URL.

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

Connected through hyperedge HE-2 (induced by source paper "toward interpretability of dual encoder models for dialogue response suggestions"):
  with members:
    - "gated group self attention for answer selection"
    - "interpretable question answering on knowledge bases and text"
    - "on the validity of self attention as explanation in transformer models"
    - "is attention interpretable"
    - "probabilistic neural symbolic models for interpretable visual question answering"
    - "on variational bounds of mutual information"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "entropy and mutual information in models of deep neural networks"

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"
    - "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"
    - "softtriple loss deep metric learning without triplet sampling"

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"
    - "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"
    - "additive margin softmax for face verification"

Connected through hyperedge HE-5 (induced by source paper "ifan image instance full alignment networks for adaptive object detection"):
  with members:
    - "few shot adaptive faster r cnn"
    - "learning continuous face age progression a pyramid of gans"
    - "exploring object relation in mean teacher for cross domain detection"
    - "automatic adaptation of object detectors to new domains using self training"
    - "semantic foggy scene understanding with synthetic data"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "pixel and feature level based domain adaption for object detection in autonomous driving"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-6 (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"
    - "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"
    - "spottune transfer learning through adaptive fine tuning"

Connected through hyperedge HE-7 (induced by source paper "unifying specialist image embedding into universal image embedding"):
  with members:
    - "learning metrics from teachers compact networks for image embedding"
    - "relational knowledge distillation"
    - "unifying heterogeneous classifiers with distillation"
    - "learning deep representations with probabilistic knowledge transfer"
    - "knowledge concentration learning 100k object classifiers in a single cnn"
    - "no fuss distance metric learning using proxies"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-8 (induced by source paper "instance cross entropy for deep metric learning"):
  with members:
    - "ranked list loss 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"
    - "learning towards minimum hyperspherical energy"

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)