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: "quick detection of nodes with large degrees"
  Abstract: Our goal is to quickly find top $k$ lists of nodes with the largest degrees in large complex networks. If the adjacency list of the network is known (not often the case in complex networks), a deterministic algorithm to find a node with the largest degree requires an average complexity of O(n), where $n$ is the number of nodes in the network. Even this modest complexity can be very high for large complex networks. We propose to use the random walk based method. We show theoretically and by numerical experiments that for large networks the random walk method finds good quality top lists of nodes with high probability and with computational savings of orders of magnitude. We also propose stopping criteria for the random walk method which requires very little knowledge about the structure of the network.

Connected through hyperedge HE-1 (induced by source paper "star sampling with and without replacement"):
  with members:
    - "a markov chain model for the search time for max degree nodes in a graph using a biased random walk"
    - "a survey and taxonomy of graph sampling"
    - "walking on a graph with a magnifying glass stratified sampling via weighted random walks"
    - "benefits of bias towards better characterization of network sampling"
    - "estimating and sampling graphs with multidimensional random walks"
    - "search in power law networks"

Connected through hyperedge HE-2 (induced by source paper "localization in seeded pagerank"):
  with members:
    - "bidirectional pagerank estimation from average case to worst case"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "a scalable generative graph model with community structure"
    - "pagerank beyond the web"
    - "sublinear algorithms for local graph centrality estimation"
    - "fast matrix computations for pairwise and columnwise commute times and katz scores"
    - "a sequential algorithm for generating random graphs"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-3 (induced by source paper "strong localization in personalized pagerank vectors"):
  with members:
    - "heat kernel based community detection"
    - "fast matrix computations for pairwise and columnwise commute times and katz scores"
    - "a sequential algorithm for generating random graphs"

Connected through hyperedge HE-4 (induced by source paper "personalized pagerank with node dependent restart"):
  with members:
    - "generalized optimization framework for graph based semi supervised learning"
    - "co authorship networks in the digital library research community"

Connected through hyperedge HE-5 (induced by source paper "distribution and dependence of extremes in network sampling processes"):
  with members:
    - "studying social networks at scale macroscopic anatomy of the twitter social graph"
    - "empirical validation of the buckley osthus model for the web host graph degree and edge distributions"

Connected through hyperedge HE-6 (induced by source paper "quick detection of high degree entities in large directed networks"):
  with members:
    - "the power of local information in social networks"
    - "fast incremental and personalized pagerank"

Connected through hyperedge HE-7 (induced by source paper "bayesian discovery of threat networks"):
  with members:
    - "discrete signal processing on graphs frequency analysis"
    - "graph spectra and the detectability of community structure in networks"
    - "empirical comparison of algorithms for network community detection"
    - "a local spectral method for graphs with applications to improving graph partitions and exploring data graphs locally"
    - "topology for distributed inference on graphs"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving 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)