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 large evolving networks"
  Abstract: Betweenness centrality ranks the importance of nodes by their participation in all shortest paths of the network. Therefore computing exact betweenness values is impractical in large networks. For static networks, approximation based on randomly sampled paths has been shown to be significantly faster in practice. However, for dynamic networks, no approximation algorithm for betweenness centrality is known that improves on static recomputation. We address this deficit by proposing two incremental approximation algorithms (for weighted and unweighted connected graphs) which provide a provable guarantee on the absolute approximation error. Processing batches of edge insertions, our algorithms yield significant speedups up to a factor of $10^4$ compared to restarting the approximation. This is enabled by investing memory to store and efficiently update shortest paths. As a building block, we also propose an asymptotically faster algorithm for updating the SSSP problem in unweighted graphs. Our experimental study shows that our algorithms are the first to make in-memory computation of a betweenness ranking practical for million-edge semi-dynamic networks. Moreover, our results show that the accuracy is even better than the theoretical guarantees in terms of absolutes errors and the rank of nodes is well preserved, in particular for those with high betweenness.

Connected through hyperedge HE-1 (induced by source paper "simple and fast distributed computation of betweenness centrality"):
  with members:
    - "distributed estimation and control of node centrality in undirected asymmetric networks"
    - "distributed algorithms for directed betweenness centrality and all pairs shortest paths"
    - "distributed exact weighted all pairs shortest paths in near linear time"
    - "distributed algorithms for computation of centrality measures in complex networks"
    - "quadratic and near quadratic lower bounds for the congest model"
    - "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"
    - "fully dynamic approximation of betweenness centrality"
    - "scalable online betweenness centrality in evolving graphs"

Connected through hyperedge HE-2 (induced by source paper "guidelines for experimental algorithmics in network analysis"):
  with members:
    - "a gpu based solution to fast calculation of betweenness centrality on large weighted networks"
    - "shared memory parallel subgraph enumeration"
    - "kadabra is an adaptive algorithm for betweenness via random approximation"
    - "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"
    - "route planning in transportation networks"
    - "networkit a tool suite for large scale complex network analysis"
    - "scalable online betweenness centrality in evolving graphs"
    - "axioms for centrality"

Connected through hyperedge HE-3 (induced by source paper "customer churn prediction in telecom using machine learning in big data platform"):
  with members:
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-4 (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"
    - "approximating betweenness centrality in fully dynamic networks"
    - "approximation algorithms for reducing the spectral radius to control epidemic spread"
    - "networkit a tool suite for large scale complex network analysis"

Connected through hyperedge HE-5 (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"
    - "approximating betweenness centrality in fully dynamic networks"
    - "gunrock a high performance graph processing library on the gpu"

Connected through hyperedge HE-6 (induced by source paper "efficient exact and approximate algorithms for computing betweenness centrality in directed graphs"):
  with members:
    - "discriminative distance based network indices with application to link prediction"
    - "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"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-7 (induced by source paper "to be connected or not to be connected that is the minimum inefficiency subgraph problem"):
  with members:
    - "the minimum wiener connector problem"
    - "scalable online betweenness centrality in evolving graphs"
    - "recipe recommendation using ingredient networks"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Connected through hyperedge HE-8 (induced by source paper "faster betweenness centrality updates in evolving networks"):
  with members:
    - "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"
    - "betweenness centrality incremental and faster"

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.SI(Social and Information Networks)