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: "leveraging weakly annotated data for fashion image retrieval and label prediction"
  Abstract: In this paper, we present a method to learn a visual representation adapted for e-commerce products. Based on weakly supervised learning, our model learns from noisy datasets crawled on e-commerce website catalogs and does not require any manual labeling. We show that our representation can be used for downward classification tasks over clothing categories with different levels of granularity. We also demonstrate that the learnt representation is suitable for image retrieval. We achieve nearly state-of-art results on the DeepFashion In-Shop Clothes Retrieval and Categories Attributes Prediction [12] tasks, without using the provided training set.

Connected through hyperedge HE-1 (induced by source paper "noiserank unsupervised label noise reduction with dependence models"):
  with members:
    - "deep self learning from noisy labels"
    - "billion scale semi supervised learning for image classification"
    - "are anchor points really indispensable in label noise learning"
    - "an effective label noise model for dnn text classification"
    - "unsupervised label noise modeling and loss correction"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "joint optimization framework for learning with noisy labels"

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 "hard aware fashion attribute classification"):
  with members:
    - "msg gan multi scale gradient gan for stable image synthesis"
    - "deep imbalanced attribute classification using visual attention aggregation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "imbalanced deep learning by minority class incremental rectification"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "in defense of the triplet loss for person re identification"
    - "progressive growing of gans for improved quality stability and variation"
    - "good semi supervised learning that requires a bad gan"

Connected through hyperedge HE-4 (induced by source paper "learning with average precision training image retrieval with a listwise loss"):
  with members:
    - "local descriptors optimized for average precision"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "mihash online hashing with mutual information"
    - "in defense of the triplet loss for person re identification"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "vse improving visual semantic embeddings with hard negatives"
    - "fine tuning cnn image retrieval with no human annotation"
    - "no fuss distance metric learning using proxies"

Connected through hyperedge HE-5 (induced by source paper "semi supervised feature level attribute manipulation for fashion image retrieval"):
  with members:
    - "combination of multiple global descriptors for image retrieval"
    - "learning type aware embeddings for fashion compatibility"
    - "hard aware point to set deep metric for person re identification"
    - "give me a hint navigating image databases using human in the loop feedback"
    - "began boundary equilibrium generative adversarial networks"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "squeeze and excitation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "who where and what to wear extracting fashion knowledge from social media"):
  with members:
    - "low resource name tagging learned with weakly labeled data"
    - "kgat knowledge graph attention network for recommendation"
    - "person re identification with metric learning using privileged information"
    - "yolov3 an incremental improvement"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "joint optimization framework for learning with noisy labels"
    - "aesthetic based clothing recommendation"
    - "transnfcm translation based neural fashion compatibility modeling"

Connected through hyperedge HE-7 (induced by source paper "two stream multi task network for fashion recognition"):
  with members:
    - "model free tracking with deep appearance and motion features integration"
    - "learning fashion compatibility with bidirectional lstms"
    - "a survey on multi task learning"
    - "unconstrained fashion landmark detection via hierarchical recurrent transformer networks"
    - "stacked hourglass networks for human pose estimation"
    - "fashion landmark detection in the wild"
    - "convolutional two stream network fusion for video action recognition"
    - "cross domain image retrieval with a dual attribute aware ranking network"

Connected through hyperedge HE-8 (induced by source paper "fashion image retrieval with capsule networks"):
  with members:
    - "deep metric learning with hierarchical triplet loss"
    - "attention based ensemble for deep metric learning"
    - "clothing retrieval with visual attention model"
    - "dynamic routing between capsules"
    - "hard aware deeply cascaded embedding"
    - "cross domain image retrieval with a dual attribute aware ranking network"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)