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: "a picture tells a thousand words about you user interest profiling from user generated visual content"
  Abstract: Inference of online social network users' attributes and interests has been an active research topic. Accurate identification of users' attributes and interests is crucial for improving the performance of personalization and recommender systems. Most of the existing works have focused on textual content generated by the users and have successfully used it for predicting users' interests and other identifying attributes. However, little attention has been paid to user generated visual content (images) that is becoming increasingly popular and pervasive in recent times. We posit that images posted by users on online social networks are a reflection of topics they are interested in and propose an approach to infer user attributes from images posted by them. We analyze the content of individual images and then aggregate the image-level knowledge to infer user-level interest distribution. We employ image-level similarity to propagate the label information between images, as well as utilize the image category information derived from the user created organization structure to further propagate the category-level knowledge for all images. A real life social network dataset created from Pinterest is used for evaluation and the experimental results demonstrate the effectiveness of our proposed approach.

Connected through hyperedge HE-1 (induced by source paper "user preference prediction in visual data on mobile devices"):
  with members:
    - "efficient facial representations for age gender and identity recognition in organizing photo albums using multi output convnet"
    - "unconstrained face identification using maximum likelihood of distances between deep off the shelf features"
    - "deep learning based large scale visual recommendation and search for e commerce"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learnable pooling with context gating for video classification"
    - "visual discovery at pinterest"
    - "neural aggregation network for video face recognition"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-2 (induced by source paper "how to become instagram famous post popularity prediction with dual attention"):
  with members:
    - "rumor detection and classification for twitter data"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "time matters multi scale temporalization of social media popularity"
    - "multimodal residual learning for visual qa"
    - "hierarchical question image co attention for visual question answering"
    - "dynamic coattention networks for question answering"
    - "show attend and tell neural image caption generation with visual attention"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "image based appraisal of real estate properties"):
  with members:
    - "deepwalk online learning of social representations"
    - "going deeper with convolutions"
    - "neural machine translation by jointly learning to align and translate"
    - "show and tell a neural image caption generator"
    - "fully convolutional networks for semantic segmentation"
    - "speech recognition with deep recurrent neural networks"
    - "recognizing image style"
    - "practical recommendations for gradient based training of deep architectures"

Connected through hyperedge HE-4 (induced by source paper "a selfie is worth a thousand words mining personal patterns behind user selfie posting behaviours"):
  with members:
    - "what the language you tweet says about your occupation"
    - "on diffusion restricted social network a measurement study of wechat moments"
    - "deep residual learning for image recognition"
    - "identifying relevant messages in a twitter based citizen channel for natural disaster situations"
    - "the lifecycle and cascade of wechat social messaging groups"
    - "the digital life of walkable streets"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "beyond classification latent user interests profiling from visual contents analysis"):
  with members:
    - "an image is worth more than a thousand favorites surfacing the hidden beauty of flickr pictures"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "distributed representations of sentences and documents"
    - "caffe convolutional architecture for fast feature embedding"
    - "gender identity and lexical variation in social media"

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.SI(Social and Information Networks)