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: "fast and exact top k search for random walk with restart"
  Abstract: Graphs are fundamental data structures and have been employed for centuries to model real-world systems and phenomena. Random walk with restart (RWR) provides a good proximity score between two nodes in a graph, and it has been successfully used in many applications such as automatic image captioning, recommender systems, and link prediction. The goal of this work is to find nodes that have top-k highest proximities for a given node. Previous approaches to this problem find nodes efficiently at the expense of exactness. The main motivation of this paper is to answer, in the affirmative, the question, `Is it possible to improve the search time without sacrificing the exactness?'. Our solution, {it K-dash}, is based on two ideas: (1) It computes the proximity of a selected node efficiently by sparse matrices, and (2) It skips unnecessary proximity computations when searching for the top-k nodes. Theoretical analyses show that K-dash guarantees result exactness. We perform comprehensive experiments to verify the efficiency of K-dash. The results show that K-dash can find top-k nodes significantly faster than the previous approaches while it guarantees exactness.

Connected through hyperedge HE-1 (induced by source paper "a survey of state management in big data processing systems"):
  with members:
    - "elasticutor rapid elasticity for realtime stateful stream processing"
    - "incremental query processing on big data streams"
    - "integrative dynamic reconfiguration in a parallel stream processing engine"
    - "frogwild fast pagerank approximations on graph engines"
    - "incremental computation with names"
    - "s store streaming meets transaction processing"
    - "tolerating correlated failures in massively parallel stream processing engines"
    - "when two choices are not enough balancing at scale in distributed stream processing"

Connected through hyperedge HE-2 (induced by source paper "fast spectral ranking for similarity search"):
  with members:
    - "unifying local and non local signal processing with graph cnns"
    - "geometric deep learning going beyond euclidean data"
    - "end to end learning of deep visual representations for image retrieval"
    - "deep image retrieval learning global representations for image search"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "efficient diffusion on region manifolds recovering small objects with compact cnn representations"
    - "fast exact and multi scale inference for semantic image segmentation with deep gaussian crfs"

Connected through hyperedge HE-3 (induced by source paper "fast and accurate random walk with restart on dynamic graphs with guarantees"):
  with members:
    - "supervised and extended restart in random walks for ranking and link prediction in networks"
    - "tpa fast scalable and accurate method for approximate random walk with restart on billion scale graphs"
    - "a comparative study of matrix factorization and random walk with restart in recommender systems"
    - "approximate personalized pagerank on dynamic graphs"
    - "fast incremental and personalized pagerank"

Connected through hyperedge HE-4 (induced by source paper "tpa fast scalable and accurate method for approximate random walk with restart on billion scale graphs"):
  with members:
    - "a comparative study of matrix factorization and random walk with restart in recommender systems"
    - "personalized pagerank estimation and search a bidirectional approach"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-5 (induced by source paper "powerwalk scalable personalized pagerank via random walks with vertex centric decomposition"):
  with members:
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "fast incremental and personalized pagerank"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-6 (induced by source paper "the minimum wiener connector problem"):
  with members:
    - "identifying high betweenness centrality nodes in large social networks"
    - "defining and evaluating network communities based on ground truth"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"
    - "balancing minimum spanning trees and shortest path trees"

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.DB(Databases)