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: "large scale visual recommendations from street fashion images"
  Abstract: We describe a completely automated large scale visual recommendation system for fashion. Our focus is to efficiently harness the availability of large quantities of online fashion images and their rich meta-data. Specifically, we propose four data driven models in the form of Complementary Nearest Neighbor Consensus, Gaussian Mixture Models, Texture Agnostic Retrieval and Markov Chain LDA for solving this problem. We analyze relative merits and pitfalls of these algorithms through extensive experimentation on a large-scale data set and baseline them against existing ideas from color science. We also illustrate key fashion insights learned through these experiments and show how they can be employed to design better recommendation systems. Finally, we also outline a large-scale annotated data set of fashion images (Fashion-136K) that can be exploited for future vision research.

Connected through hyperedge HE-1 (induced by source paper "a strong baseline for fashion retrieval with person re identification models"):
  with members:
    - "fashion retrieval via graph reasoning networks on a similarity pyramid"
    - "mildnet a lightweight single scaled deep ranking architecture"
    - "auto reid searching for a part aware convnet for person re identification"
    - "learning generalisable omni scale representations for person re identification"
    - "a strong baseline and batch normalization neck for deep person re identification"
    - "spatial temporal person re identification"
    - "two at once enhancing learning and generalization capacities via ibn net"
    - "learning embeddings for product visual search with triplet loss and online sampling"

Connected through hyperedge HE-2 (induced by source paper "improving outfit recommendation with co supervision of fashion generation"):
  with members:
    - "outfit generation and style extraction via bidirectional lstm and autoencoder"
    - "aesthetic based clothing recommendation"
    - "neural compatibility modeling with attentive knowledge distillation"
    - "creating capsule wardrobes from fashion images"
    - "visually aware fashion recommendation and design with generative image models"
    - "learning fashion compatibility with bidirectional lstms"
    - "cvae gan fine grained image generation through asymmetric training"
    - "mining fashion outfit composition using an end to end deep learning approach on set data"

Connected through hyperedge HE-3 (induced by source paper "pog personalized outfit generation for fashion recommendation at alibaba ifashion"):
  with members:
    - "bert4rec sequential recommendation with bidirectional encoder representations from transformer"
    - "learning type aware embeddings for fashion compatibility"
    - "deep contextualized word representations"
    - "deepstyle multimodal search engine for fashion and interior design"
    - "outfit generation and style extraction via bidirectional lstm and autoencoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "self attentive sequential recommendation"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"

Connected through hyperedge HE-4 (induced by source paper "c gan complementary fashion item recommendation"):
  with members:
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "streetstyle exploring world wide clothing styles from millions of photos"
    - "style2vec representation learning for fashion items from style sets"
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"
    - "learning fashion compatibility with bidirectional lstms"
    - "mining fashion outfit composition using an end to end deep learning approach on set data"
    - "fashion forward forecasting visual style in fashion"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-5 (induced by source paper "learning fashion compatibility across apparel categories for outfit recommendation"):
  with members:
    - "video2shop exactly matching clothes in videos to online shopping images"
    - "learning type aware embeddings for fashion compatibility"
    - "neural compatibility modeling with attentive knowledge distillation"
    - "creating capsule wardrobes from fashion images"
    - "exploiting restricted boltzmann machines and deep belief networks in compressed sensing"
    - "learning fashion compatibility with bidirectional lstms"
    - "mining fashion outfit composition using an end to end deep learning approach on set data"
    - "learning compatibility across categories for heterogeneous item recommendation"

Connected through hyperedge HE-6 (induced by source paper "mmalfm explainable recommendation by leveraging reviews and images"):
  with members:
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "transnets learning to transform for recommendation"
    - "sherlock sparse hierarchical embeddings for visually aware one class collaborative filtering"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"

Connected through hyperedge HE-7 (induced by source paper "visually aware recommendation with aesthetic features"):
  with members:
    - "nais neural attentive item similarity model for recommendation"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "a lamp adaptive layout aware multi patch deep convolutional neural network for photo aesthetic assessment"
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-8 (induced by source paper "explainable fashion recommendation with joint outfit matching and comment generation"):
  with members:
    - "explainable recommendation via multi task learning in opinionated text data"
    - "measuring semantic coherence of a conversation"
    - "learning to generate reviews and discovering sentiment"
    - "neural compatibility modeling with attentive knowledge distillation"
    - "selective encoding for abstractive sentence summarization"
    - "deep recurrent generative decoder for abstractive text summarization"
    - "attention is all you need"
    - "neural collaborative filtering"

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)