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: "sherlock sparse hierarchical embeddings for visually aware one class collaborative filtering"
  Abstract: Building successful recommender systems requires uncovering the underlying dimensions that describe the properties of items as well as users' preferences toward them. In domains like clothing recommendation, explaining users' preferences requires modeling the visual appearance of the items in question. This makes recommendation especially challenging, due to both the complexity and subtlety of people's 'visual preferences,' as well as the scale and dimensionality of the data and features involved. Ultimately, a successful model should be capable of capturing considerable variance across different categories and styles, while still modeling the commonalities explained by `global' structures in order to combat the sparsity (e.g. cold-start), variability, and scale of real-world datasets. Here, we address these challenges by building such structures to model the visual dimensions across different product categories. With a novel hierarchical embedding architecture, our method accounts for both high-level (colorfulness, darkness, etc.) and subtle (e.g. casualness) visual characteristics simultaneously.

Connected through hyperedge HE-1 (induced by source paper "dressing as a whole outfit compatibility learning based on node wise graph neural networks"):
  with members:
    - "session based recommendation with graph neural network"
    - "constructing narrative event evolutionary graph for script event prediction"
    - "situation recognition with graph neural networks"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "compatibility family learning for item recommendation and generation"
    - "style2vec representation learning for fashion items from style sets"
    - "learning fashion compatibility with bidirectional lstms"

Connected through hyperedge HE-2 (induced by source paper "the resale price prediction of secondhand jewelry items using a multi modal deep model with iterative co attention"):
  with members:
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "murel multimodal relational reasoning for visual question answering"
    - "deepstyle multimodal search engine for fashion and interior design"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "deep learning based large scale visual recommendation and search for e commerce"
    - "beyond bilinear generalized multi modal factorized high order pooling for visual question answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"

Connected through hyperedge HE-3 (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"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "vista a visually socially and temporally aware model for artistic recommendation"

Connected through hyperedge HE-4 (induced by source paper "explainable fashion recommendation a semantic attribute region guided approach"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning from history and present next item recommendation via discriminatively exploiting user behaviors"
    - "explainable recommendation a survey and new perspectives"
    - "visually explainable recommendation"
    - "aesthetic based clothing recommendation"
    - "a hierarchical attention model for social contextual image recommendation"
    - "visually aware fashion recommendation and design with generative image models"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"

Connected through hyperedge HE-5 (induced by source paper "learning preferences and demands in visual recommendation"):
  with members:
    - "a simple convolutional generative network for next item recommendation"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "inferring networks of substitutable and complementary products"
    - "session based recommendations with recurrent neural networks"
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "image based recommendations on styles and substitutes"
    - "gated feedback recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "a correlative denoising autoencoder to model social influence for top n recommender system"):
  with members:
    - "deep residual learning for image recognition"
    - "collaborative deep learning for recommender systems"
    - "a convolutional neural network for modelling sentences"

Connected through hyperedge HE-7 (induced by source paper "interacting attention gated recurrent networks for recommendation"):
  with members:
    - "recurrent poisson factorization for temporal recommendation"
    - "neural collaborative filtering"
    - "meta prod2vec product embeddings using side information for recommendation"
    - "temporal attention gated model for robust sequence classification"
    - "item2vec neural item embedding for collaborative filtering"
    - "session based recommendations with recurrent neural networks"
    - "image based recommendations on styles and substitutes"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-8 (induced by source paper "trust aware top n recommender systems with correlative denoising autoencoder"):
  with members:
    - "comparative deep learning of hybrid representations for image recommendations"
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "deep residual learning for image recognition"
    - "collaborative deep learning for recommender systems"
    - "a convolutional neural network for modelling sentences"
    - "bpr bayesian personalized ranking from implicit feedback"

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)