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 vertex similarity index for better personalized recommendation"):
  Abstract: Recommender systems benefit us in tackling the problem of information overload by predicting our potential choices among diverse niche objects. So far, a variety of personalized recommendation algorithms have been proposed and most of them are based on similarities, such as collaborative filtering and mass diffusion. Here, we propose a novel vertex similarity index named CosRA, which combines advantages of both the cosine index and the resource-allocation (RA) index. By applying the CosRA index to real recommender systems including MovieLens, Netflix and RYM, we show that the CosRA-based method has better performance in accuracy, diversity and novelty than some benchmark methods. Moreover, the CosRA index is free of parameters, which is a significant advantage in real applications. Further experiments show that the introduction of two turnable parameters cannot remarkably improve the overall performance of the CosRA index.

Includes members:
  Member "evaluating user reputation in online rating systems via an iterative group based ranking method":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on adversary robust consensus protocols through joint agent interactions")
  Member "group based ranking method for online rating systems with spamming attacks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a trust based recommendation method using network diffusion processes")
  Member "ultra accurate collaborative information filtering via directed user similarity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a trust based recommendation method using network diffusion processes")
  Member "information filtering by similarity preferential diffusion processes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a trust based recommendation method using network diffusion processes")
  Member "extracting the information backbone in online system":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fast extraction of the backbone of projected bipartite networks to aid community detection")
  Member "solving the apparent diversity accuracy dilemma of recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

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.IR(Information Retrieval)