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: "networkit an interactive tool suite for high performance network analysis"
  Abstract: We introduce NetworKit, an open-source software package for high-performance analysis of large complex networks. Complex networks are equally attractive and challenging targets for data mining, and novel algorithmic solutions as well as parallelization are required to handle data sets containing billions of connections. Our goal for NetworKit is to package results of our algorithm engineering efforts and put them into the hands of domain experts. NetworKit is a hybrid combining the performance of kernels written in C++ with a convenient interactive interface written in Python. The package supports shared-memory parallelism and scales from notebooks to compute servers. In comparison with related software, we propose NetworKit as a package geared towards large networks and satisfying three important criteria: High performance, interactive workflows and integration into an ecosystem of tested tools for data analysis and scientific computation. The current feature set includes analytics kernels such as degree distribution, connected components, clustering coefficients, community detection, k-core decomposition, degree assortativity and multiple centrality indices. Moreover, NetworKit comes with a collection of graph generators. With the current release, we present and open up the project to a community of both algorithm engineers and domain experts

Connected through hyperedge HE-1 (induced by source paper "efficient community detection in boolean composed multiplex networks"):
  with members:
    - "community detection link prediction and layer interdependence in multilayer networks"
    - "modularity in complex multilayer networks with multiple aspects a static perspective"
    - "generalized measures for the evaluation of community detection methods"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "mining attribute structure correlated patterns in large attributed graphs"
    - "clustering with multi layer graphs a spectral perspective"

Connected through hyperedge HE-2 (induced by source paper "multiscale planar graph generation"):
  with members:
    - "single and multi level network sparsification by algebraic distance"
    - "generating realistic scaled complex networks"
    - "generating scaled replicas of real world complex networks"
    - "a multilevel bilinear programming algorithm for the vertex separator problem"
    - "recent advances in graph partitioning"
    - "community structure and scale free collections of erdős renyi graphs"
    - "advanced coarsening schemes for graph partitioning"
    - "multiscale network generation"

Connected through hyperedge HE-3 (induced by source paper "practical minimum cut algorithms"):
  with members:
    - "local flow partitioning for faster edge connectivity"
    - "concurrent hash tables fast and general"
    - "deterministic global minimum cut of a simple graph in near linear time"
    - "semi external algorithms for graph partitioning and clustering"
    - "on the analysis of a label propagation algorithm for community detection"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"
    - "minimum cuts in near linear time"

Connected through hyperedge HE-4 (induced by source paper "shared memory exact minimum cuts"):
  with members:
    - "practical minimum cut algorithms"
    - "local flow partitioning for faster edge connectivity"
    - "concurrent hash tables fast and general"
    - "deterministic global minimum cut of a simple graph in near linear time"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"
    - "an o m algorithm for cores decomposition of networks"

Connected through hyperedge HE-5 (induced by source paper "computing top k closeness centrality faster in unweighted graphs"):
  with members:
    - "a note on the complexity of computing the number of reachable vertices in a digraph"
    - "fast and simple computation of top k closeness centralities"
    - "approximation and fixed parameter subquadratic algorithms for radius and diameter"
    - "average distance queries through weighted samples in graphs and metric spaces high scalability with tight statistical guarantees"
    - "powers of tensors and fast matrix multiplication"
    - "networkit a tool suite for large scale complex network analysis"
    - "popular conjectures imply strong lower bounds for dynamic problems"
    - "into the square on the complexity of quadratic time solvable problems"

Connected through hyperedge HE-6 (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"
    - "approximating betweenness centrality in fully dynamic networks"
    - "fully dynamic approximation of betweenness centrality"
    - "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-7 (induced by source paper "kadabra is an adaptive algorithm for betweenness via random approximation"):
  with members:
    - "dynamic merging of frontiers for accelerating the evaluation of betweenness centrality"
    - "computing top k closeness centrality faster in unweighted graphs"
    - "metropolis hastings algorithms for estimating betweenness centrality in large networks"
    - "an axiomatic and an average case analysis of algorithms and heuristics for metric properties of graphs"
    - "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"
    - "bubing massive crawling for the masses"
    - "fully dynamic approximation of betweenness centrality"
    - "into the square on the complexity of quadratic time solvable problems"

Connected through hyperedge HE-8 (induced by source paper "single and multi level network sparsification by algebraic distance"):
  with members:
    - "structure preserving sparsification of social networks"
    - "a survey and taxonomy of graph sampling"
    - "discovering social circles in ego networks"
    - "network sampling from static to streaming graphs"
    - "large social networks can be targeted for viral marketing with small seed sets"
    - "advanced coarsening schemes for graph partitioning"
    - "relaxation based coarsening and multiscale graph organization"
    - "spectral sparsification of graphs"

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)