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: "image based recommendations on styles and substitutes"
  Abstract: Humans inevitably develop a sense of the relationships between objects, some of which are based on their appearance. Some pairs of objects might be seen as being alternatives to each other (such as two pairs of jeans), while others may be seen as being complementary (such as a pair of jeans and a matching shirt). This information guides many of the choices that people make, from buying clothes to their interactions with each other. We seek here to model this human sense of the relationships between objects based on their appearance. Our approach is not based on fine-grained modeling of user annotations but rather on capturing the largest dataset possible and developing a scalable method for uncovering human notions of the visual relationships within. We cast this as a network inference problem defined on graphs of related images, and provide a large-scale dataset for the training and evaluation of the same. The system we develop is capable of recommending which clothes and accessories will go well together (and which will not), amongst a host of other applications.

Connected through hyperedge HE-1 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis"):
  with members:
    - "session based recommendation with graph neural network"
    - "contextual hybrid session based news recommendation with recurrent neural networks"
    - "on the importance of news content representation in hybrid neural session based recommender systems"
    - "deep learning based recommender system a survey and new perspectives"
    - "neurec on nonlinear transformation for personalized ranking"
    - "sequence aware recommender systems"
    - "evaluation of session based recommendation algorithms"
    - "multi pointer co attention networks for recommendation"

Connected through hyperedge HE-2 (induced by source paper "spectral graph attention network"):
  with members:
    - "power up robust graph convolutional network against evasion attacks based on graph powering"
    - "stabilizing gans with octave convolutions"
    - "a restricted black box adversarial framework towards attacking graph embedding models"
    - "lanczosnet multi scale deep graph convolutional networks"
    - "measuring and relieving the over smoothing problem for graph neural networks from the topological view"
    - "graph neural networks with convolutional arma filters"
    - "dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems"
    - "semi supervised graph classification a hierarchical graph perspective"

Connected through hyperedge HE-3 (induced by source paper "learning diverse fashion collocation by neural graph filtering"):
  with members:
    - "context aware visual compatibility prediction"
    - "learning type aware embeddings for fashion compatibility"
    - "person re identification with deep similarity guided graph neural network"
    - "learning human object interactions by graph parsing neural networks"
    - "dynamic graph cnn for learning on point clouds"
    - "videos as space time region graphs"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "focal loss for dense object detection"

Connected through hyperedge HE-4 (induced by source paper "regularizing reasons for outfit evaluation with gradient penalty"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "toward explainable fashion recommendation"
    - "explainable fashion recommendation a semantic attribute region guided approach"
    - "context aware visual compatibility prediction"
    - "regularizing black box models for improved interpretability"
    - "dressing as a whole outfit compatibility learning based on node wise graph neural networks"
    - "outfit compatibility prediction and diagnosis with multi layered comparison network"
    - "learning type aware embeddings for fashion compatibility"

Connected through hyperedge HE-5 (induced by source paper "self enhanced gnn improving graph neural networks using model outputs"):
  with members:
    - "geom gcn geometric graph convolutional networks"
    - "measuring and relieving the over smoothing problem for graph neural networks from the topological view"
    - "unifying graph convolutional neural networks and label propagation"
    - "graph neural networks exponentially lose expressive power for node classification"
    - "fast graph representation learning with pytorch geometric"
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "graph neural networks a review of methods and applications"
    - "pitfalls of graph neural network evaluation"

Connected through hyperedge HE-6 (induced by source paper "adversarial multimodal representation learning for click through rate prediction"):
  with members:
    - "transfer learning with dynamic adversarial adaptation network"
    - "neural news recommendation with attentive multi view learning"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "multimodal machine learning a survey and taxonomy"
    - "improved recurrent neural networks for session based recommendations"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "the effects of gender signals and performance in online product reviews"):
  with members:
    - "gender differences in participation and reward on stack overflow"
    - "gendered behavior as a disadvantage in open source software development"
    - "why do men get more attention exploring factors behind success in an online design community"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "inferring gender from names on the web a comparative evaluation of gender detection methods"
    - "why should i trust you explaining the predictions of any classifier"
    - "it s a man s wikipedia assessing gender inequality in an online encyclopedia"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-8 (induced by source paper "hotelrec a novel very large scale hotel recommendation dataset"):
  with members:
    - "multi dimensional explanation of reviews"
    - "neural graph collaborative filtering"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "neural collaborative filtering"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "understanding neural networks through representation erasure"
    - "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.CV(Computer Vision and Pattern Recognition)