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: "comparative deep learning of hybrid representations for image recommendations"
  Abstract: In many image-related tasks, learning expressive and discriminative representations of images is essential, and deep learning has been studied for automating the learning of such representations. Some user-centric tasks, such as image recommendations, call for effective representations of not only images but also preferences and intents of users over images. Such representations are termed hybrid and addressed via a deep learning approach in this paper. We design a dual-net deep network, in which the two sub-networks map input images and preferences of users into a same latent semantic space, and then the distances between images and users in the latent space are calculated to make decisions. We further propose a comparative deep learning (CDL) method to train the deep network, using a pair of images compared against one user to learn the pattern of their relative distances. The CDL embraces much more training data than naive deep learning, and thus achieves superior performance than the latter, with no cost of increasing network complexity. Experimental results with real-world data sets for image recommendations have shown the proposed dual-net network and CDL greatly outperform other state-of-the-art image recommendation solutions.

Connected through hyperedge HE-1 (induced by source paper "autoemb automated embedding dimensionality search in streaming recommendations"):
  with members:
    - "adabert task adaptive bert compression with differentiable neural architecture search"
    - "mixed dimension embeddings with application to memory efficient recommendation systems"
    - "neural input search for large scale recommendation models"
    - "deep learning based recommender system a survey and new perspectives"
    - "regularized evolution for image classifier architecture search"
    - "neural architecture optimization"
    - "path level network transformation for efficient architecture search"
    - "darts differentiable architecture search"

Connected through hyperedge HE-2 (induced by source paper "deep unified multimodal embeddings for understanding both content and users in social media networks"):
  with members:
    - "integrating text and image determining multimodal document intent in instagram posts"
    - "align2ground weakly supervised phrase grounding guided by image caption alignment"
    - "universal dependency parsing from scratch"
    - "deep learning based recommender system a survey and new perspectives"
    - "exploring the limits of weakly supervised pretraining"
    - "a comprehensive survey of deep learning for image captioning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attributed social network embedding"

Connected through hyperedge HE-3 (induced by source paper "a hierarchical attention model for social contextual image recommendation"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "nais neural attentive item similarity model for recommendation"
    - "neural collaborative filtering"
    - "a survey on network embedding"
    - "related pins at pinterest the evolution of a real world recommender system"
    - "a context aware attention network for interactive question answering"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "autosvd an efficient hybrid collaborative filtering model via contractive auto encoders"

Connected through hyperedge HE-4 (induced by source paper "adversarial training towards robust multimedia recommender system"):
  with members:
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "adversarial personalized ranking for recommendation"
    - "visually explainable recommendation"
    - "nais neural attentive item similarity model for recommendation"
    - "transnfcm translation based neural fashion compatibility modeling"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "adversarial dropout for supervised and semi supervised learning"

Connected through hyperedge HE-5 (induced by source paper "hierarchical attention network for visually aware food recommendation"):
  with members:
    - "discrete factorization machines for fast feature based recommendation"
    - "adversarial personalized ranking for recommendation"
    - "aesthetic based clothing recommendation"
    - "grab pay and eat semantic food detection for smart restaurants"
    - "neural collaborative filtering"
    - "item silk road recommending items from information domains to social users"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"

Connected through hyperedge HE-6 (induced by source paper "d sempre learning deep semantic preserving embeddings for user interests social contents modeling"):
  with members:
    - "deep ctr prediction in display advertising"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "going deeper with convolutions"
    - "fisher vectors derived from hybrid gaussian laplacian mixture models for image annotation"
    - "caffe convolutional architecture for fast feature embedding"
    - "collaborative topic regression with social matrix factorization for recommendation systems"

Connected through hyperedge HE-7 (induced by source paper "visually aware fashion recommendation and design with generative image models"):
  with members:
    - "learning fashion compatibility with bidirectional lstms"
    - "mining fashion outfit composition using an end to end deep learning approach on set data"
    - "fashion forward forecasting visual style in fashion"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "improved techniques for training gans"
    - "generative adversarial text to image synthesis"
    - "vista a visually socially and temporally aware model for artistic recommendation"

Connected through hyperedge HE-8 (induced by source paper "energy based sequence gans for recommendation and their connection to imitation learning"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "reinforcement learning with deep energy based policies"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "generative adversarial imitation learning"
    - "energy based generative adversarial network"
    - "a connection between generative adversarial networks inverse reinforcement learning and energy based models"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "session based recommendations with 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.CV(Computer Vision and Pattern Recognition)