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: "vbpr visual bayesian personalized ranking from implicit feedback"
  Abstract: Modern recommender systems model people and items by discovering or `teasing apart' the underlying dimensions that encode the properties of items and users' preferences toward them. Critically, such dimensions are uncovered based on user feedback, often in implicit form (such as purchase histories, browsing logs, etc.); in addition, some recommender systems make use of side information, such as product attributes, temporal information, or review text. However one important feature that is typically ignored by existing personalized recommendation and ranking methods is the visual appearance of the items being considered. In this paper we propose a scalable factorization model to incorporate visual signals into predictors of people's opinions, which we apply to a selection of large, real-world datasets. We make use of visual features extracted from product images using (pre-trained) deep networks, on top of which we learn an additional layer that uncovers the visual dimensions that best explain the variation in people's feedback. This not only leads to significantly more accurate personalized ranking methods, but also helps to alleviate cold start issues, and qualitatively to analyze the visual dimensions that influence people's opinions.

Connected through hyperedge HE-1 (induced by source paper "recommender systems based on generative adversarial networks a problem driven perspective"):
  with members:
    - "deep adversarial social recommendation"
    - "adversarial mahalanobis distance based attentive song recommender for automatic playlist continuation"
    - "kgat knowledge graph attention network for recommendation"
    - "deep learning to address candidate generation and cold start challenges in recommender systems a research survey"
    - "generating reliable friends via adversarial training to improve social recommendation"
    - "cold start playlist recommendation with multitask learning"
    - "exs explainable search using local model agnostic interpretability"
    - "adversarial collaborative auto encoder for top n recommendation"

Connected through hyperedge HE-2 (induced by source paper "metaselector meta learning for recommendation with user level adaptive model selection"):
  with members:
    - "a closer look at few shot classification"
    - "neural graph collaborative filtering"
    - "meta learning pac bayes priors in model averaging"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "one at a time a meta learning recommender system for recommendation algorithm selection on micro level"
    - "federated meta learning with fast convergence and efficient communication"
    - "neural factorization machines for sparse predictive analytics"

Connected through hyperedge HE-3 (induced by source paper "optimizing item and subgroup configurations for social aware vr shopping"):
  with members:
    - "solving linear programs in the current matrix multiplication time"
    - "neural collaborative filtering"
    - "spmc socially aware personalized markov chains for sparse sequential recommendation"
    - "a multilayer network dataset of interaction and influence spreading in a virtual world"
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "submodular and supermodular multi labeling and vertex happiness"
    - "from group recommendations to group formation"
    - "when social influence meets item inference"

Connected through hyperedge HE-4 (induced by source paper "on the effectiveness of convolutional autoencoders on image based personalized recommender systems"):
  with members:
    - "grab pay and eat semantic food detection for smart restaurants"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "modeling embedding dimension correlations via convolutional neural collaborative filtering"):
  with members:
    - "temporal relational ranking for stock prediction"
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "fast matrix factorization with non uniform weights on missing data"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "product based neural networks for user response prediction over multi field categorical data"
    - "outer product based neural collaborative filtering"
    - "adversarial personalized ranking for recommendation"
    - "a simple convolutional generative network for next item recommendation"

Connected through hyperedge HE-6 (induced by source paper "neural graph collaborative filtering"):
  with members:
    - "relational collaborative filtering modeling multiple item relations for recommendation"
    - "kgat knowledge graph attention network for recommendation"
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
    - "graph convolutional neural networks for web scale recommender systems"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "collaborative memory network for recommendation systems"
    - "representation learning on graphs with jumping knowledge networks"

Connected through hyperedge HE-7 (induced by source paper "deep collaborative filtering with multi aspect information in heterogeneous networks"):
  with members:
    - "prsim sublinear time simrank computation on large power law graphs"
    - "deep learning based recommender system a survey and new perspectives"
    - "outer product based neural collaborative filtering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural brane neural bayesian personalized ranking for attributed network embedding"
    - "neural factorization machines for sparse predictive analytics"
    - "attention is all you need"
    - "neural collaborative filtering"

Connected through hyperedge HE-8 (induced by source paper "a survey on session based recommender systems"):
  with members:
    - "recurrent neural networks with top k gains for session based recommendations"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "session aware information embedding for e commerce product recommendation"
    - "leveraging long and short term information in content aware movie recommendation"
    - "contextual sequence modeling for recommendation with recurrent neural networks"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "neural attentive session based recommendation"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"

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)