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 "closure operators and spam resistance for pagerank"):
  Abstract: We study the spammablility of ranking functions on the web. Although graph-theoretic ranking functions, such as Hubs and Authorities and PageRank exist, there is no graph theoretic notion of how spammable such functions are. #R##N#We introduce a very general cost model that only depends on the observation that changing the links of a page that you own is free, whereas changing the links on pages owned by others requires effort or money. We define spammability to be the ratio between the amount of benefit one receives for one's spamming efforts and the amount of effort/money one must spend to spam. The more effort/money it takes to get highly ranked, the less spammable the function. #R##N#Our model helps explain why both hubs and authorities and standard PageRank are very easy to spam. Although standard PageRank is easy to spam, we show that there exist spam-resistant PageRanks. Specifically, we propose a ranking method, Min-k-PPR, that is the component-wise min of a set of personalized PageRanks centered on k trusted sites. Our main results are that Min-k-PPR is, itself, a type of PageRank and that it is expensive to spam. #R##N#We elucidate a surprisingly elegant algebra for PageRank. We define the space of all possible PageRanks and show that this space is closed under some operations. Most notably, we show that PageRanks are closed under (normalized) component-wise min, which establishes that (normalized) Min-k-PPRis a PageRank. This algebraic structure is also key to demonstrating the spam resistance of Min-k-PPR.

Includes members:
  Member "pagerank beyond the web":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nonlinearity networks a 2020 vision")
  Member "fast incremental and personalized pagerank":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "prince provider side interpretability with counterfactual explanations in recommender systems")

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.DS(Data Structures and Algorithms)