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: "group based ranking method for online rating systems with spamming attacks"
  Abstract: The ranking problem has attracted much attention in real systems. How to design a robust ranking method is especially significant for online rating systems under the threat of spamming attacks. By building reputation systems for users, many well-performed ranking methods have been applied to address this issue. In this letter, we propose a group-based ranking method that evaluates users' reputations based on their grouping behaviors. More specifically, users are assigned with high reputation scores if they always fall into large rating groups. Results on three real data sets indicate that the present method is more accurate and robust than the correlation-based method in the presence of spamming attacks.

Connected through hyperedge HE-1 (induced by source paper "a trust based recommendation method using network diffusion processes"):
  with members:
    - "evaluating user reputation in online rating systems via an iterative group based ranking method"
    - "a vertex similarity index for better personalized recommendation"
    - "network based recommendation algorithms a review"
    - "ultra accurate collaborative information filtering via directed user similarity"
    - "multi linear interactive matrix factorization"
    - "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 "evaluating user reputation in online rating systems via an iterative group based ranking method"):
  with members:
    - "a vertex similarity index for better personalized recommendation"
    - "an iterative method for calculating robust rating scores"
    - "towards an objective ranking in online reputation systems the effect of the rating projection"
    - "a robust ranking algorithm to spamming"
    - "the effect of discrete vs continuous valued ratings on reputation and ranking systems"
    - "iterative filtering for a dynamical reputation system"

Connected through hyperedge HE-3 (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"
    - "ultra accurate collaborative information filtering via directed user similarity"
    - "information filtering by similarity preferential diffusion processes"
    - "extracting the information backbone in online system"
    - "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)