human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "extracting the information backbone in online system"
  Abstract: Information overload is a serious problem in modern society and many solutions such as recommender system have been proposed to filter out irrelevant information. In the literature, researchers have been mainly dedicated to improving the recommendation performance (accuracy and diversity) of the algorithms while they have overlooked the influence of topology of the online user-object bipartite networks. In this paper, we find that some information provided by the bipartite networks is not only redundant but also misleading. With such “less can be more” feature, we design some algorithms to improve the recommendation performance by eliminating some links from the original networks. Moreover, we propose a hybrid method combining the time-aware and topology-aware link removal algorithms to extract the backbone which contains the essential information for the recommender systems. From the practical point of view, our method can improve the performance and reduce the computational time of the recommendation system, thus improving both of their effectiveness and efficiency.

Connected through hyperedge HE-1 (induced by source paper "a vertex similarity index for better personalized recommendation"):
  with members:
    - "evaluating user reputation in online rating systems via an iterative group based ranking method"
    - "group based ranking method for online rating systems with spamming attacks"
    - "ultra accurate collaborative information filtering via directed user similarity"
    - "information filtering by similarity preferential diffusion processes"
    - "solving the apparent diversity accuracy dilemma of recommender systems"

Connected through hyperedge HE-2 (induced by source paper "fast extraction of the backbone of projected bipartite networks to aid community detection"):
  with members:
    - "predicting item popularity analysing local clustering behaviour of users"
    - "identifying influential nodes in bipartite networks using the clustering coefficient"

Connected through hyperedge HE-3 (induced by source paper "a fast and simple method of extracting the backbone of one mode projections an aid in the detection of communities"):
  with members:
    - "predicting item popularity analysing local clustering behaviour of users"

Connected through hyperedge HE-4 (induced by source paper "information filtering in sparse online systems recommendation via semi local diffusion"):
  with members:
    - "solving the apparent diversity accuracy dilemma of recommender systems"

Question: Please tell me which class the center node 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)