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: "learning unified embedding for apparel recognition"
  Abstract: In apparel recognition, specialized models (e.g. models trained for a particular vertical like dresses) can significantly outperform general models (i.e. models that cover a wide range of verticals). Therefore, deep neural network models are often trained separately for different verticals. However, using specialized models for different verticals is not scalable and expensive to deploy. This paper addresses the problem of learning one unified embedding model for multiple object verticals (e.g. all apparel classes) without sacrificing accuracy. The problem is tackled from two aspects: training data and training difficulty. On the training data aspect, we figure out that for a single model trained with triplet loss, there is an accuracy sweet spot in terms of how many verticals are trained together. To ease the training difficulty, a novel learning scheme is proposed by using the output from specialized models as learning targets so that L2 loss can be used instead of triplet loss. This new loss makes the training easier and make it possible for more efficient use of the feature space. The end result is a unified model which can achieve the same retrieval accuracy as a number of separate specialized models, while having the model complexity as one. The effectiveness of our approach is shown in experiments.

Connected through hyperedge HE-1 (induced by source paper "event sequence metric learning"):
  with members:
    - "e t rnn applying deep learning to credit loan applications"
    - "metric learning for adversarial robustness"
    - "learning a unified embedding for visual search at pinterest"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "a comparison study of credit card fraud detection supervised versus unsupervised"
    - "prediction is very hard especially about conversion predicting user purchases from clickstream data in fashion e commerce"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "fashion retrieval via graph reasoning networks on a similarity pyramid"):
  with members:
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "netizen style commenting on fashion photos dataset and diversity measures"
    - "end to end deep kronecker product matching for person re identification"
    - "video2shop exactly matching clothes in videos to online shopping images"
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "deep randomized ensembles for metric learning"
    - "improving the annotation of deepfashion images for fine grained attribute recognition"
    - "person re identification with deep similarity guided graph neural network"

Connected through hyperedge HE-3 (induced by source paper "learning type aware embeddings for fashion compatibility"):
  with members:
    - "sampling matters in deep embedding learning"
    - "self supervised learning of visual features through embedding images into text topic spaces"
    - "be your own prada fashion synthesis with structural coherence"
    - "dress like a star retrieving fashion products from videos"
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"
    - "leveraging weakly annotated data for fashion image retrieval and label prediction"
    - "learning fashion compatibility with bidirectional lstms"
    - "mining fashion outfit composition using an end to end deep learning approach on set data"

Connected through hyperedge HE-4 (induced by source paper "low shot learning with imprinted weights"):
  with members:
    - "normface l 2 hypersphere embedding for face verification"
    - "prototypical networks for few shot learning"
    - "few shot image recognition by predicting parameters from activations"
    - "no fuss distance metric learning using proxies"
    - "cosine normalization using cosine similarity instead of dot product in neural networks"
    - "matching networks for one shot learning"
    - "low shot visual recognition by shrinking and hallucinating features"
    - "facenet a unified embedding for face recognition and clustering"

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)