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 "coarse cluster enhancing collaborative recommendation for social network systems"):
  Abstract: Traditional collaborative filtering based recommender systems for social network systems bring very high demands on time complexity due to computing similarities of all pairs of users via resource usages and annotation actions, which thus strongly suppresses recommending speed. In this paper, to overcome this drawback, we propose a novel approach, namely coarse cluster   that partitions similar users and associated items at a high speed to enhance user-based collaborative filtering, and then develop a fast collaborative user model for the social tagging systems. The experimental results based on Delicious dataset show that the proposed model is able to dramatically reduce the processing time cost greater than 90%90% and relatively improve the accuracy in comparison with the ordinary user-based collaborative filtering, and is robust for the initial parameter. Most importantly, the proposed model can be conveniently extended by introducing more users’ information (e.g., profiles) and practically applied for the large-scale social network systems to enhance the recommending speed without accuracy loss.

Includes members:
  Member "ultra accurate collaborative information filtering via directed user similarity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a trust based recommendation method using network diffusion processes")
  Member "personalized recommendation with corrected similarity":
    also connected through other hyperedges:
      (none)
  Member "information filtering via collaborative user clustering modeling":
    also connected through other hyperedges:
      (none)
  Member "uncovering the information core in recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "preference modeling by exploiting latent components of ratings")
  Member "tag aware recommender systems a state of the art survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "leveraging tagging and rating for recommendation rmf meets weighted diffusion on tripartite graphs")
  Member "collaborative filtering with diffusion based similarity on 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")
  Member "solving the apparent diversity accuracy dilemma of recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (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.IR(Information Retrieval)