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: "a robust ranking algorithm to spamming"
  Abstract: Ranking problem of web-based rating systems has attracted much attention. A good ranking algorithm should be robust against spammer attack. Here we proposed a correlation-based reputation algorithm to solve the ranking problem of such rating systems where user votes some objects with ratings. In this algorithm, the reputation of a user is iteratively determined by the correlation coefficient between his/her rating vector and the corresponding objects' weighted average rating vector. Comparing with iterative refinement (IR) and mean score algorithm, results for both artificial and real data indicate that the present algorithm shows a higher robustness against spammer attack.

Connected through hyperedge HE-1 (induced by source paper "accumulative time based ranking method to reputation evaluation in information networks"):
  with members:
    - "measuring the academic reputation through citation networks via pagerank"
    - "birank towards ranking on bipartite graphs"
    - "information filtering via balanced diffusion on bipartite networks"

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"
    - "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"
    - "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 "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"
    - "iterative filtering for a dynamical reputation system"

Connected through hyperedge HE-4 (induced by source paper "an iterative method for calculating robust rating scores"):
  with members:
    - "serf and turf crowdturfing for fun and profit"
    - "iterative filtering for a dynamical reputation system"

Connected through hyperedge HE-5 (induced by source paper "uncovering the information core in recommender systems"):
  with members:
    - "information filtering in sparse online systems recommendation via semi local diffusion"
    - "solving the apparent diversity accuracy dilemma of recommender systems"

Connected through hyperedge HE-6 (induced by source paper "a two step recommendation algorithm via iterative local least squares"):
  with members:
    - "solving the apparent diversity accuracy dilemma of recommender systems"
    - "personalized recommendation via integrated diffusion on user item tag tripartite graphs"
    - "improved collaborative filtering algorithm via information transformation"

Connected through hyperedge HE-7 (induced by source paper "a framework of algorithms computing the bias and prestige of nodes in trust networks"):
  with members:
    - "the slashdot zoo mining a social network with negative edges"

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)