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 "network based recommendation algorithms a review"):
  Abstract: Recommender systems are a vital tool that helps us to overcome the information overload problem. They are being used by most e-commerce web sites and attract the interest of a broad scientific community. A recommender system uses data on users’ past preferences to choose new items that might be appreciated by a given individual user. While many approaches to recommendation exist, the approach based on a network representation of the input data has gained considerable attention in the past. We review here a broad range of network-based recommendation algorithms and for the first time compare their performance on three distinct real datasets. We present recommendation topics that go beyond the mere question of which algorithm to use–such as the possible influence of recommendation on the evolution of systems that use it–and finally discuss open research directions and challenges.

Includes members:
  Member "a vertex similarity index for better personalized recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "trapping problem on star type graphs with applications")
  Member "modeling mutual feedback between users and recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "quantifying the effects of recommendation systems")
  Member "information filtering by similarity preferential diffusion processes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a trust based recommendation method using network diffusion processes")
  Member "information filtering in sparse online systems recommendation via semi local diffusion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "coverage analysis of information dissemination in throwbox augmented dtn")
  Member "network based information filtering algorithms ranking and recommendation":
    also connected through other hyperedges:
      (none)
  Member "trend prediction in temporal bipartite networks the case of movielens netflix and digg":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "enhancing countries fitness with recommender systems on the international trade network")
  Member "tag aware recommender systems a state of the art survey":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "coarse cluster enhancing collaborative recommendation for social network systems")
  Member "solving the apparent diversity accuracy dilemma of recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)