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: "aesthetic based clothing recommendation"
  Abstract: Recently, product images gain increasing concern in clothing recommendation since the visual appearance of the items have a significant impact on consumers’ decision. Existing models usually extract conventional features, such as convolutional neural network (CNN) features, scale-invariant feature transform (SIFT) features, and color histograms, to represent item image characters and capture user visual preferences. However, one important feature, aesthetic feature, is typically ignored. It is vital in recommendation since users’ decision depends largely on if the clothing is in line with their aesthetic while the conventional image features cannot portray this directly. To bridge this gap, we propose to introduce aesthetic information which is more related with users’ preference into the field of clothing recommender system. To do so, we first present the aesthetic features extracted by an pre-trained neural network, which is a brain inspired deep structure trained for aesthetic assessment task. Considering the aesthetic preference shows diversity with different people and time, we propose a novel tensor factorization model as a basic model and then incorporate the aesthetic features into it. Finally, extensive experiments on real-world datasets demonstrate that our approach can capture the aesthetic preference of consumers and outperform several state-of-the-art models significantly.

Connected through hyperedge HE-1 (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-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"
    - "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"
    - "neural attentive session based recommendation"

Connected through hyperedge HE-3 (induced by source paper "revisiting image aesthetic assessment via self supervised feature learning"):
  with members:
    - "effective aesthetics prediction with multi level spatially pooled features"
    - "high fidelity image generation with fewer labels"
    - "nima neural image assessment"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "unsupervised representation learning by predicting image rotations"
    - "tracking emerges by colorizing videos"
    - "predicting aesthetic score distribution through cumulative jensen shannon divergence"
    - "colorization as a proxy task for visual understanding"

Connected through hyperedge HE-4 (induced by source paper "interest related item similarity model based on multimodal data for top n recommendation"):
  with members:
    - "adversarial training towards robust multimedia recommender system"
    - "achieving efficient and secure data acquisition for cloud supported internet of things in smart grid"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "nais neural attentive item similarity model for recommendation"
    - "a multimodal recommender system for large scale assortment generation in e commerce"
    - "attention is all you need"
    - "neural collaborative filtering"
    - "wide deep learning for recommender systems"

Connected through hyperedge HE-5 (induced by source paper "who where and what to wear extracting fashion knowledge from social media"):
  with members:
    - "low resource name tagging learned with weakly labeled data"
    - "kgat knowledge graph attention network for recommendation"
    - "person re identification with metric learning using privileged information"
    - "yolov3 an incremental improvement"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "joint optimization framework for learning with noisy labels"
    - "transnfcm translation based neural fashion compatibility modeling"
    - "neural compatibility modeling with attentive knowledge distillation"

Connected through hyperedge HE-6 (induced by source paper "λopt learn to regularize recommender models in finer levels"):
  with members:
    - "warm up cold start advertisements improving ctr predictions via learning to learn id embeddings"
    - "adversarial personalized ranking for recommendation"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "entity embeddings of categorical variables"
    - "a generic coordinate descent framework for learning from implicit feedback"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-7 (induced by source paper "which ads to show advertisement image assessment with auxiliary information via multi step modality fusion"):
  with members:
    - "what is wrong with scene text recognition model comparisons dataset and model analysis"
    - "character region awareness for text detection"
    - "multimodal dual attention memory for video story question answering"
    - "nima neural image assessment"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "nsml meet the mlaas platform with a real world case study"
    - "modulating early visual processing by language"

Connected through hyperedge HE-8 (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"
    - "a hierarchical attention model for social contextual image recommendation"
    - "visually aware fashion recommendation and design with generative image models"
    - "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"

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)