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: "fashion dna merging content and sales data for recommendation and article mapping"
  Abstract: We present a method to determine Fashion DNA, coordinate vectors locating fashion items in an abstract space. Our approach is based on a deep neural network architecture that ingests curated article information such as tags and images, and is trained to predict sales for a large set of frequent customers. In the process, a dual space of customer style preferences naturally arises. Interpretation of the metric of these spaces is straightforward: The product of Fashion DNA and customer style vectors yields the forecast purchase likelihood for the customer-item pair, while the angle between Fashion DNA vectors is a measure of item similarity. Importantly, our models are able to generate unbiased purchase probabilities for fashion items based solely on article information, even in absence of sales data, thus circumventing the "cold-start problem" of collaborative recommendation approaches. Likewise, it generalizes easily and reliably to customers outside the training set. We experiment with Fashion DNA models based on visual and/or tag item data, evaluate their recommendation power, and discuss the resulting article similarities.

Connected through hyperedge HE-1 (induced by source paper "session based complementary fashion recommendations"):
  with members:
    - "recurrent neural networks with top k gains for session based recommendations"
    - "inferring complementary products from baskets and browsing sessions"
    - "deep style match for complementary recommendation"
    - "tensorflow a system for large scale machine learning"
    - "session based recommendations with recurrent neural networks"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "sizenet weakly supervised learning of visual size and fit in fashion images"):
  with members:
    - "a unified model with structured output for fashion images classification"
    - "studio2shop from studio photo shoots to fashion articles"
    - "a hierarchical bayesian model for size recommendation in fashion"
    - "outfit generation and style extraction via bidirectional lstm and autoencoder"
    - "rise randomized input sampling for explanation of black box models"
    - "deep residual learning for image recognition"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "disentangling multiple conditional inputs in gans"):
  with members:
    - "dna gan learning disentangled representations from multi attribute images"
    - "progressive growing of gans for improved quality stability and variation"
    - "be your own prada fashion synthesis with structural coherence"
    - "deep photo style transfer"
    - "improved training of wasserstein gans"
    - "image to image translation with conditional adversarial networks"
    - "generative visual manipulation on the natural image manifold"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "studio2shop from studio photo shoots to fashion articles"):
  with members:
    - "fashion landmark detection in the wild"
    - "large scale visual recommendations from street fashion images"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-5 (induced by source paper "understanding fashionability what drives sales of a style"):
  with members:
    - "towards predicting the likeability of fashion images"

Connected through hyperedge HE-6 (induced by source paper "footwear size recommendation system"):
  with members:
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "fashion forward forecasting visual style in fashion"):
  with members:
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"
    - "automatic attribute discovery with neural activations"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "when was that made"
    - "learning visual clothing style with heterogeneous dyadic co occurrences"
    - "image based recommendations on styles and substitutes"
    - "cross domain image retrieval with a dual attribute aware ranking network"
    - "who are the devils wearing prada in new york city"

Connected through hyperedge HE-8 (induced by source paper "an lstm based dynamic customer model for fashion recommendation"):
  with members:
    - "deep residual learning for image recognition"

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.IR(Information Retrieval)