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: "random walk domination in large graphs problem definitions and fast solutions"
  Abstract: We introduce and formulate two types of random-walk domination problems in graphs motivated by a number of applications in practice (e.g., item-placement problem in online social network, Ads-placement problem in advertisement networks, and resource-placement problem in P2P networks). Specifically, given a graph $G$, the goal of the first type of random-walk domination problem is to target $k$ nodes such that the total hitting time of an $L$-length random walk starting from the remaining nodes to the targeted nodes is minimal. The second type of random-walk domination problem is to find $k$ nodes to maximize the expected number of nodes that hit any one targeted node through an $L$-length random walk. We prove that these problems are two special instances of the submodular set function maximization with cardinality constraint problem. To solve them effectively, we propose a dynamic-programming (DP) based greedy algorithm which is with near-optimal performance guarantee. The DP-based greedy algorithm, however, is not very efficient due to the expensive marginal gain evaluation. To further speed up the algorithm, we propose an approximate greedy algorithm with linear time complexity w.r.t.\ the graph size and also with near-optimal performance guarantee. The approximate greedy algorithm is based on a carefully designed random-walk sampling and sample-materialization techniques. Extensive experiments demonstrate the effectiveness, efficiency and scalability of the proposed algorithms.

Connected through hyperedge HE-1 (induced by source paper "optimizing node discovery on networks problem definitions fast algorithms and observations"):
  with members:
    - "optimal tagging with markov chain optimization"
    - "powerwalk scalable personalized pagerank via random walks with vertex centric decomposition"
    - "scalable betweenness centrality maximization via sampling"
    - "absorbing random walk centrality theory and algorithms"
    - "computing classic closeness centrality at scale"
    - "axioms for centrality"
    - "using friends as sensors to detect global scale contagious outbreaks"
    - "a tractable approach to finding closest truncated commute time neighbors in large graphs"

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.SI(Social and Information Networks)