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 "graph based collaborative ranking"):
  Abstract: GRank is a novel framework, designed for recommendation based on rank data.GRank handles the sparsity problem of neighbor-based collaborative ranking.GRank uses the novel TPG graph structure to model users' choice context.GRank directly ranks itemsfor a target userusing personalized PageRank in TPG.GRank improves [email protected] up to 9% compared to other collaborative ranking methods. Data sparsity, that is a common problem in neighbor-based collaborative filtering domain, usually complicates the process of item recommendation. This problem is more serious in collaborative ranking domain, in which calculating the users' similarities and recommending items are based on ranking data. Some graph-based approaches have been proposed to address the data sparsity problem, but they suffer from two flaws. First, they fail to correctly model the users' priorities, and second, they can't be used when the only available data is a set of ranking instead of rating values.In this paper, we propose a novel graph-based approach, called GRank, that is designed for collaborative ranking domain. GRank can correctly model users' priorities in a new tripartite graph structure, and analyze it to directly infer a recommendation list. The experimental results show a significant improvement in recommendation quality compared to the state of the art graph-based recommendation algorithms and other collaborative ranking techniques.

Includes members:
  Member "bpr bayesian personalized ranking from implicit feedback":
    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 "collaborative filtering with diffusion based similarity on tripartite graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "recommending investors for new startups by integrating network diffusion and investors domain preference")
  Member "solving the apparent diversity accuracy dilemma of recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "personalized recommendation via integrated diffusion on user item tag tripartite graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "recommending investors for new startups by integrating network diffusion and investors domain preference")

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)