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: "deep metric learning with bier boosting independent embeddings robustly"
  Abstract: Learning similarity functions between image pairs with deep neural networks yields highly correlated activations of embeddings. In this work, we show how to improve the robustness of such embeddings by exploiting the independence within ensembles. To this end, we divide the last embedding layer of a deep network into an embedding ensemble and formulate training this ensemble as an online gradient boosting problem. Each learner receives a reweighted training sample from the previous learners. Further, we propose two loss functions which increase the diversity in our ensemble. These loss functions can be applied either for weight initialization or during training. Together, our contributions leverage large embedding sizes more effectively by significantly reducing correlation of the embedding and consequently increase retrieval accuracy of the embedding. Our method works with any differentiable loss function and does not introduce any additional parameters during test time. We evaluate our metric learning method on image retrieval tasks and show that it improves over state-of-the-art methods on the CUB 200-2011, Cars-196, Stanford Online Products, In-Shop Clothes Retrieval and VehicleID 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"
    - "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 "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"
    - "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-3 (induced by source paper "context aware recommendations for televisions using deep embeddings with relaxed n pairs loss objective"):
  with members:
    - "deep joint embeddings of context and content for recommendation"
    - "deep learning based recommender system a survey and new perspectives"
    - "grasp2vec learning object representations from self supervised grasping"
    - "the importance of context when recommending tv content dataset and algorithms"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "wide deep learning for recommender systems"
    - "meta prod2vec product embeddings using side information for recommendation"

Connected through hyperedge HE-4 (induced by source paper "optimizing rank based metrics with blackbox differentiation"):
  with members:
    - "learning with average precision training image retrieval with a listwise loss"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "towards accurate one stage object detection with ap loss"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "sodeep a sorting deep net to learn ranking loss surrogates"
    - "mediaeval 2018 predicting media memorability task"
    - "yolov3 an incremental improvement"
    - "deep randomized ensembles for metric learning"

Connected through hyperedge HE-5 (induced by source paper "hybrid attention based decoupled metric learning for zero shot image retrieval"):
  with members:
    - "energy confused adversarial metric learning for zero shot image retrieval and clustering"
    - "signal to noise ratio a robust distance metric for deep metric learning"
    - "zero shot sketch image hashing"
    - "discriminative learning of latent features for zero shot recognition"
    - "almn deep embedding learning with geometrical virtual point generating"
    - "attention based ensemble for deep metric learning"
    - "sampling matters in deep embedding learning"
    - "deep metric learning with angular loss"

Connected through hyperedge HE-6 (induced by source paper "divide and conquer the embedding space for metric learning"):
  with members:
    - "improving spatiotemporal self supervision by deep reinforcement learning"
    - "learning discriminative features with multiple granularities for person re identification"
    - "harmonious attention network for person re identification"
    - "deep randomized ensembles for metric learning"
    - "mining on manifolds metric learning without labels"
    - "deep group shuffling random walk for person re identification"
    - "deep metric learning with hierarchical triplet loss"
    - "deep unsupervised learning of visual similarities"

Connected through hyperedge HE-7 (induced by source paper "energy confused adversarial metric learning for zero shot image retrieval and clustering"):
  with members:
    - "zero shot sketch image hashing"
    - "almn deep embedding learning with geometrical virtual point generating"
    - "sampling matters in deep embedding learning"
    - "noisy softmax improving the generalization ability of dcnn via postponing the early softmax saturation"
    - "deep metric learning with angular loss"
    - "no fuss distance metric learning using proxies"
    - "smart mining for deep metric learning"
    - "disturblabel regularizing cnn on the loss layer"

Connected through hyperedge HE-8 (induced by source paper "relational knowledge distillation"):
  with members:
    - "object part attention model for fine grained image classification"
    - "model compression via distillation and quantization"
    - "learning to navigate for fine grained classification"
    - "attention based ensemble for deep metric learning"
    - "label refinery improving imagenet classification through label progression"
    - "sampling matters in deep embedding learning"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "apprentice using knowledge distillation techniques to improve low precision network accuracy"

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)