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 "an iterative method for calculating robust rating scores"):
  Abstract: Online rating systems are widely used to facilitate making decisions on the web. For fame or profit, people may try to manipulate such systems by posting unfair evaluations. Therefore, determining objective rating scores of products or services becomes a very important yet difficult problem. Existing solutions are mostly majority based, also employing temporal analysis and clustering techniques. However, they are still vulnerable to sophisticated collaborative attacks. In this paper we propose an iterative rating algorithm which is very robust against collusion attacks as well as random and biased raters. Unlike previous iterative methods, our method is not based on comparing submitted evaluations to an approximation of the final rating scores, and it entirely decouples credibility assessment of the cast evaluations from the ranking itself. This makes it more robust against sophisticated collusion attacks than the previous iterative filtering algorithms. We provide a rigorous proof of convergence of our algorithm based on the existence of a fixed point of a continuous mapping which also happens to be a stationary point of a constrained optimization objective. We have implemented and tested our rating method using both simulated data as well as real world movie rating data. Our tests demonstrate that our model calculates realistic rating scores even in the presence of massive collusion attacks and outperforms well-known algorithms in the area. The results of applying our algorithm on the real-world data obtained from MovieLens conforms highly with the rating scores given by Rotten Tomatoes movie critics as domain experts for movies.

Includes members:
  Member "a robust ranking algorithm to spamming":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "accumulative time based ranking method to reputation evaluation in information networks")
  Member "serf and turf crowdturfing for fun and profit":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "text analysis in adversarial settings does deception leave a stylistic trace")
  Member "iterative filtering for a dynamical reputation system":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "evaluating user reputation in online rating systems via an iterative group based ranking method")

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)