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: "efficient algorithms for personalized pagerank"
  Abstract: We present new, more efficient algorithms for estimating random walk scores such as Personalized PageRank from a given source node to one or several target nodes. These scores are useful for personalized search and recommendations on networks including social networks, user-item networks, and the web. Past work has proposed using Monte Carlo or using linear algebra to estimate scores from a single source to every target, making them inefficient for a single pair. Our contribution is a new bidirectional algorithm which combines linear algebra and Monte Carlo to achieve significant speed improvements. On a diverse set of six graphs, our algorithm is 70x faster than past state-of-the-art algorithms. We also present theoretical analysis: while past algorithms require $\Omega(n)$ time to estimate a random walk score of typical size $\frac{1}{n}$ on an $n$-node graph to a given constant accuracy, our algorithm requires only $O(\sqrt{m})$ expected time for an average target, where $m$ is the number of edges, and is provably accurate. #R##N#In addition to our core bidirectional estimator for personalized PageRank, we present an alternative algorithm for undirected graphs, a generalization to arbitrary walk lengths and Markov Chains, an algorithm for personalized search ranking, and an algorithm for sampling random paths from a given source to a given set of targets. We expect our bidirectional methods can be extended in other ways and will be useful subroutines in other graph analysis problems.

Connected through hyperedge HE-1 (induced by source paper "efficient algorithms for approximate single source personalized pagerank queries"):
  with members:
    - "distributed algorithms for fully personalized pagerank on large graphs"
    - "prsim sublinear time simrank computation on large power law graphs"
    - "tpa fast scalable and accurate method for approximate random walk with restart on billion scale graphs"
    - "personalized pagerank estimation and search a bidirectional approach"
    - "approximate personalized pagerank on dynamic graphs"
    - "fast distributed pagerank computation"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-2 (induced by source paper "approximate personalized pagerank on dynamic graphs"):
  with members:
    - "personalized pagerank estimation and search a bidirectional approach"
    - "frogwild fast pagerank approximations on graph engines"
    - "bidirectional pagerank estimation from average case to worst case"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "on the complexity of the monte carlo method for incremental pagerank"
    - "pagerank beyond the web"
    - "defining and evaluating network communities based on ground truth"
    - "fast incremental and personalized pagerank"

Connected through hyperedge HE-3 (induced by source paper "personalized pagerank estimation and search a bidirectional approach"):
  with members:
    - "fast bidirectional probability estimation in markov models"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "personalized pagerank to a target node"
    - "fast incremental and personalized pagerank"

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)