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 "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.

Includes members:
  Member "personalized pagerank estimation and search a bidirectional approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "empirical policy evaluation with supergraphs")
  Member "a note on the pagerank of undirected graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "graph similarity using pagerank and persistent homology")
  Member "fast bidirectional probability estimation in markov models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "empirical policy evaluation with supergraphs")
  Member "fast distributed pagerank computation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "walking randomly massively and efficiently")
  Member "learning fast mixing models for structured prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reified context models")
  Member "bidirectional pagerank estimation from average case to worst case":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep node ranking structural network embedding and end to end node classification")
  Member "heat kernel based community detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "connectivity driven communication in multi agent reinforcement learning through diffusion processes on graphs")
  Member "fast ppr scaling personalized pagerank estimation for large graphs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient algorithms for approximate single source personalized pagerank queries")

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)