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: "iterative filtering for a dynamical reputation system"
  Abstract: The paper introduces a novel iterative method that assigns a rep- utation to n +m items: n raters and m objects. Each rater evaluates a subset of objects leading to a n m rating matrix with a certain sparsity pattern. From this rating matrix we give a nonlinear formula to dene the reputation of raters and objects. We also provide an iterative algorithm that superlin- early converges to the unique vector of reputations and this for any rating matrix. In contrast to classical outliers detection, no evaluation is discarded in this method but each one is taken into account with dierent weights for the reputation of the objects. The complexity of one iteration step is linear in the number of evaluations, making our algorithm ecient for large data set. Experiments show good robustness of the reputation of the objects against cheaters and spammers and good detection properties of cheaters and spam- mers.

Connected through hyperedge HE-1 (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"
    - "group based ranking method for online rating systems with spamming attacks"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "group based ranking method for online rating systems with spamming attacks"):
  with members:
    - "ultra accurate collaborative information filtering via directed user similarity"
    - "information filtering by similarity preferential diffusion processes"
    - "a robust ranking algorithm to spamming"

Connected through hyperedge HE-3 (induced by source paper "an iterative method for calculating robust rating scores"):
  with members:
    - "a robust ranking algorithm to spamming"
    - "serf and turf crowdturfing for fun and profit"

Connected through hyperedge HE-4 (induced by source paper "rank aggregation via nuclear norm minimization"):
  with members:
    - "a tensor based method for missing traffic data completion"
    - "recovering low rank matrices from few coefficients in any basis"
    - "the power of convex relaxation near optimal matrix completion"
    - "set an algorithm for consistent matrix completion"
    - "guaranteed rank minimization via singular value projection"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-5 (induced by source paper "a robust ranking algorithm to spamming"):
  with members:
    - "building reputation systems for better ranking"

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)