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: "improved distortion and spam resistance for pagerank"
  Abstract: For a directed graph $G = (V,E)$, a ranking function, such as PageRank, provides a way of mapping elements of $V$ to non-negative real numbers so that nodes can be ordered. Brin and Page argued that the stationary distribution, $R(G)$, of a random walk on $G$ is an effective ranking function for queries on an idealized web graph. However, $R(G)$ is not defined for all $G$, and in particular, it is not defined for the real web graph. Thus, they introduced PageRank to approximate $R(G)$ for graphs $G$ with ergodic random walks while being defined on all graphs. #R##N#PageRank is defined as a random walk on a graph, where with probability $(1-\epsilon)$, a random out-edge is traversed, and with \emph{reset probability} $\epsilon$ the random walk instead restarts at a node selected using a \emph{reset vector} $\hat{r}$. Originally, $\hat{r}$ was taken to be uniform on the nodes, and we call this version UPR. #R##N#In this paper, we introduce graph-theoretic notions of quality for ranking functions, specifically \emph{distortion} and \emph{spam resistance}. We show that UPR has high distortion and low spam resistance and we show how to select an $\hat{r}$ that yields low distortion and high spam resistance.

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.DS(Data Structures and Algorithms)