human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "caffe convolutional architecture for fast feature embedding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "gender identity and lexical variation in social media":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "personalized dialogue generation with diversified traits")
  Member "visualizing and understanding convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "a tutorial on spectral clustering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximating spectral clustering via sampling a review")

Question: Please tell me which class the source paper 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)