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 "d sempre learning deep semantic preserving embeddings for user interests social contents modeling"):
  Abstract: Exponential growth of social media consumption demands effective user interests-social contents modeling for more personalized recommendation and social media summarization. However, due to the heterogeneous nature of social contents, traditional approaches lack the ability of capturing the hidden semantic correlations across these multi-modal data, which leads to semantic gaps between social content understanding and user interests. To effectively bridge the semantic gaps, we propose a novel deep learning framework for user interests-social contents modeling. We first mine and parse data, i.e. textual content, visual content, social context and social relation, from heterogeneous social media feeds. Then, we design a two-branch network to map the social contents and users into a same latent space. Particularly, the network is trained by a large margin objective that combines a cross-instance distance constraint with a within-instance semantic-preserving constraint in an end-to- end manner. At last, a Deep Semantic-Preserving Embedding (D-Sempre) is learned, and the ranking results can be given by calculating distances between social contents and users. To demonstrate the effectiveness of D-Sempre in user interests-social contents modeling, we construct a Twitter dataset and conduct extensive experiments on it. As a result, D-Sempre effectively integrates the multi-modal data from heterogeneous social media feeds and captures the hidden semantic correlations between users' interests and social contents.

Includes members:
  Member "deep ctr prediction in display advertising":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "targeted display advertising the case of preferential attachment")
  Member "comparative deep learning of hybrid representations for image recommendations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "autoemb automated embedding dimensionality search in streaming recommendations")
  Member "batch normalization accelerating deep network training by reducing internal covariate shift":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "going deeper with convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "fisher vectors derived from hybrid gaussian laplacian mixture models for image annotation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "language features matter effective language representations for vision language tasks")
  Member "caffe convolutional architecture for fast feature embedding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "collaborative topic regression with social matrix factorization for recommendation systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "semi supervised learning meets factorization learning to recommend with chain graph model")

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)