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: "computing the stationary distribution locally"
  Abstract: Computing the stationary distribution of a large finite or countably infinite state space Markov Chain has become central to many problems such as statistical inference and network analysis. Standard methods involve large matrix multiplications as in power iteration, or simulations of long random walks, as in Markov Chain Monte Carlo (MCMC). For both methods, the convergence rate is is difficult to determine for general Markov chains. Power iteration is costly, as it is global and involves computation at every state. In this paper, we provide a novel local algorithm that answers whether a chosen state in a Markov chain has stationary probability larger than some $\Delta \in (0,1)$, and outputs an estimate of the stationary probability for itself and other nearby states. Our algorithm runs in constant time with respect to the Markov chain, using information from a local neighborhood of the state on the graph induced by the Markov chain, which has constant size relative to the state space. The multiplicative error of the estimate is upper bounded by a function of the mixing properties of the Markov chain. Simulation results show Markov chains for which this method gives tight estimates.

Connected through hyperedge HE-1 (induced by source paper "loop estimator for discounted values in markov reward processes"):
  with members:
    - "approximating the stationary probability of a single state in a markov chain"

Connected through hyperedge HE-2 (induced by source paper "on the role of clustering in personalized pagerank estimation"):
  with members:
    - "personalized pagerank estimation and search a bidirectional approach"
    - "asynchronous approximation of a single component of the solution to a linear system"
    - "solving systems of linear equations locally and asynchronously"
    - "pagerank beyond the web"
    - "personalized pagerank to a target node"
    - "deltacon a principled massive graph similarity function"

Connected through hyperedge HE-3 (induced by source paper "on approximating the stationary distribution of time reversible markov chains"):
  with members:
    - "fast bidirectional probability estimation in markov models"
    - "bidirectional pagerank estimation from average case to worst case"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "solving systems of linear equations locally and asynchronously"
    - "sublinear algorithms for local graph centrality estimation"

Connected through hyperedge HE-4 (induced by source paper "efficient algorithms for personalized pagerank"):
  with members:
    - "personalized pagerank estimation and search a bidirectional approach"
    - "a note on the pagerank of undirected graphs"
    - "fast bidirectional probability estimation in markov models"
    - "fast distributed pagerank computation"
    - "learning fast mixing models for structured prediction"
    - "bidirectional pagerank estimation from average case to worst case"
    - "heat kernel based community detection"
    - "fast ppr scaling personalized pagerank estimation for large graphs"

Connected through hyperedge HE-5 (induced by source paper "fast bidirectional probability estimation in markov models"):
  with members:
    - "learning fast mixing models for structured prediction"
    - "heat kernel based community detection"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-6 (induced by source paper "sublinear algorithms for local graph centrality estimation"):
  with members:
    - "on approximating the stationary distribution of time reversible markov chains"
    - "an optimization approach to locally biased graph algorithms"
    - "personalized pagerank estimation and search a bidirectional approach"
    - "the power of local information in pagerank"
    - "simple set cardinality estimation through random sampling"
    - "fast bidirectional probability estimation in markov models"
    - "computing heat kernel pagerank and a local clustering algorithm"
    - "bidirectional pagerank estimation from average case to worst case"

Connected through hyperedge HE-7 (induced by source paper "solving systems of linear equations locally and asynchronously"):
  with members:
    - "a distributed algorithm for solving a linear algebraic equation"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "a nearly mlogn time solver for sdd linear systems"
    - "nearly linear time algorithms for preconditioning and solving symmetric diagonally dominant linear systems"

Connected through hyperedge HE-8 (induced by source paper "asynchronous approximation of a single component of the solution to a linear system"):
  with members:
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "nearly linear time algorithms for preconditioning and solving symmetric diagonally dominant linear systems"

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)