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: "approximating betweenness centrality in fully dynamic networks"
  Abstract: Betweenness is a well-known centrality measure that ranks the nodes of a network according to their participation in shortest paths. Since an exact computation is prohibitive in large networks, several approximation algorithms have been proposed. Besides that, recent years have seen the publication of dynamic algorithms for efficient recomputation of betweenness in networks that change over time. In this paper we propose the first betweenness centrality approximation algorithms with a provable guarantee on the maximum approximation error for dynamic networks. Several new intermediate algorithmic results contribute to the respective approximation algorithms: (i) new upper bounds on the vertex diameter, (ii) the first fully-dynamic algorithm for updating an approximation of the vertex diameter in undirected graphs, and (iii) an algorithm with lower time complexity for updating single-source shortest paths in unweighted graphs after a batch of edge actions. Using approximation, our algorithms are the first to make in-memory computation of betweenness in dynamic networks with millions of edges feasible. Our experiments show that our algorithms can achieve substantial speedups compared to recomputation, up to several orders of magnitude. Moreover, the approximation accuracy is usually significantly better than the theoretical guarantee in terms of absolute error. More importantly, for reasonably small approximation error thresholds, the rank of nodes is well preserved, in particular for nodes with high betweenness.

Connected through hyperedge HE-1 (induced by source paper "learning to identify high betweenness centrality nodes from scratch a novel graph neural network approach"):
  with members:
    - "representation learning on graphs with jumping knowledge networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "enhanced network embeddings via exploiting edge labels"
    - "graph edit distance computation via graph neural networks"
    - "learning structural node embeddings via diffusion wavelets"
    - "representation learning on graphs methods and applications"
    - "kadabra is an adaptive algorithm for betweenness via random approximation"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-2 (induced by source paper "updating dynamic random hyperbolic graphs in sublinear time"):
  with members:
    - "nearest neighbor searching under uncertainty ii"
    - "querying probabilistic neighborhoods in spatial data sets efficiently"
    - "generating massive complex networks with hyperbolic geometry faster in practice"
    - "generating scaled replicas of real world complex networks"
    - "sampling geometric inhomogeneous random graphs in linear time"
    - "geometric inhomogeneous random graphs"
    - "on the diameter of hyperbolic random graphs"
    - "fast generation of complex networks with underlying hyperbolic geometry"

Connected through hyperedge HE-3 (induced by source paper "improving the betweenness centrality of a node by adding links"):
  with members:
    - "computing top k closeness centrality faster in unweighted graphs"
    - "faster betweenness centrality updates in evolving networks"
    - "kadabra is an adaptive algorithm for betweenness via random approximation"
    - "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"
    - "scalable betweenness centrality maximization via sampling"
    - "approximation algorithms for reducing the spectral radius to control epidemic spread"
    - "networkit a tool suite for large scale complex network analysis"
    - "scalable online betweenness centrality in evolving graphs"

Connected through hyperedge HE-4 (induced by source paper "enabling massive deep neural networks with the graphblas"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "speeding up convolutional neural networks by exploiting the sparsity of rectifier units"
    - "novel graph processor architecture prototype system and results"
    - "mathematical foundations of the graphblas"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "gunrock a high performance graph processing library on the gpu"
    - "graphulo implementation of server side sparse matrix multiply in the accumulo database"

Connected through hyperedge HE-5 (induced by source paper "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"):
  with members:
    - "refining approximating betweenness centrality based on samplings"
    - "kadabra is an adaptive algorithm for betweenness via random approximation"
    - "fully dynamic approximation of betweenness centrality"
    - "networkit an interactive tool suite for high performance network analysis"
    - "networkit a tool suite for large scale complex network analysis"
    - "scalable online betweenness centrality in evolving graphs"
    - "approximating betweenness centrality in large evolving networks"
    - "decremental all pairs all shortest paths and betweenness centrality"

Connected through hyperedge HE-6 (induced by source paper "mathematical foundations of the graphblas"):
  with members:
    - "engineering parallel algorithms for community detection in massive networks"
    - "exploiting multiple levels of parallelism in sparse matrix matrix multiplication"
    - "gunrock a high performance graph processing library on the gpu"
    - "graphulo implementation of server side sparse matrix multiply in the accumulo database"
    - "networkit a tool suite for large scale complex network analysis"
    - "parallel graph partitioning for complex networks"
    - "parallel sparse matrix matrix multiplication and indexing implementation and experiments"

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)