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 hierarchical attention model for social contextual image recommendation"):
  Abstract: Image based social networks are among the most popular social networking services in recent years. With tremendous images uploaded everyday, understanding users' preferences on user-generated images and making recommendations have become an urgent need. In fact, many hybrid models have been proposed to fuse various kinds of side information~(e.g., image visual representation, social network) and user-item historical behavior for enhancing recommendation performance. However, due to the unique characteristics of the user generated images in social image platforms, the previous studies failed to capture the complex aspects that influence users' preferences in a unified framework. Moreover, most of these hybrid models relied on predefined weights in combining different kinds of information, which usually resulted in sub-optimal recommendation performance. To this end, in this paper, we develop a hierarchical attention model for social contextual image recommendation. In addition to basic latent user interest modeling in the popular matrix factorization based recommendation, we identify three key aspects (i.e., upload history, social influence, and owner admiration) that affect each user's latent preferences, where each aspect summarizes a contextual factor from the complex relationships between users and images. After that, we design a hierarchical attention network that naturally mirrors the hierarchical relationship (elements in each aspects level, and the aspect level) of users' latent interests with the identified key aspects. Specifically, by taking embeddings from state-of-the-art deep learning models that are tailored for each kind of data, the hierarchical attention network could learn to attend differently to more or less content. Finally, extensive experimental results on real-world datasets clearly show the superiority of our proposed model.

Includes members:
  Member "deep learning based recommender system a survey and new perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "nais neural attentive item similarity model for recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lightgcn simplifying and powering graph convolution network for recommendation")
  Member "neural collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "advances in collaborative filtering and ranking")
  Member "a survey on network embedding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to segment 3d point clouds in 2d image space")
  Member "related pins at pinterest the evolution of a real world recommender system":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "handling position bias for unbiased learning to rank in hotels search")
  Member "a context aware attention network for interactive question answering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "look before you hop conversational question answering over knowledge graphs using judicious context expansion")
  Member "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "zimm a deep learning model for long term and blurry relapses with non clinical claims data")
  Member "autosvd an efficient hybrid collaborative filtering model via contractive auto encoders":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "why not to use zero imputation correcting sparsity bias in training neural networks")

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)