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 trust based recommendation method using network diffusion processes"):
  Abstract: Abstract   A variety of rating-based recommendation methods have been extensively studied including the well-known collaborative filtering approaches and some network diffusion-based methods, however, social trust relations are not sufficiently considered when making recommendations. In this paper, we contribute to the literature by proposing a trust-based recommendation method, named CosRA+T, after integrating the information of trust relations into the resource-redistribution process. Specifically, a tunable parameter is used to scale the resources received by trusted users before the redistribution back to the objects. Interestingly, we find an optimal scaling parameter for the proposed CosRA+T method to achieve its best recommendation accuracy, and the optimal value seems to be universal under several evaluation metrics across different datasets. Moreover, results of extensive experiments on the two real-world rating datasets with trust relations, Epinions and FriendFeed, suggest that CosRA+T has a remarkable improvement in overall accuracy, diversity and novelty. Our work takes a step towards designing better recommendation algorithms by employing multiple resources of social network information.

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 "a vertex similarity index for better personalized recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "trapping problem on star type graphs with applications")
  Member "network based recommendation algorithms a review":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "recommending investors for new startups by integrating network diffusion and investors domain preference")
  Member "group based ranking method for online rating systems with spamming attacks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "evaluating user reputation in online rating systems via an iterative group based ranking method")
  Member "ultra accurate collaborative information filtering via directed user similarity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "coarse cluster enhancing collaborative recommendation for social network systems")
  Member "multi linear interactive matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "paper evolution graph multi view structural retrieval for academic literature")
  Member "information filtering by similarity preferential diffusion processes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a vertex similarity index for better personalized recommendation")
  Member "solving the apparent diversity accuracy dilemma of recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)