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 a tool suite for large scale complex network analysis"
  Abstract: We introduce NetworKit, an open-source software package for analyzing the structure of large complex networks. Appropriate algorithmic solutions are required to handle increasingly common large graph data sets containing up to billions of connections. We describe the methodology applied to develop scalable solutions to network analysis problems, including techniques like parallelization, heuristics for computationally expensive problems, efficient data structures, and modular software architecture. Our goal for the software is to package results of our algorithm engineering efforts and put them into the hands of domain experts. NetworKit is implemented as a hybrid combining the kernels written in C++ with a Python front end, enabling integration into the Python ecosystem of tested tools for data analysis and scientific computing. The package provides a wide range of functionality (including common and novel analytics algorithms and graph generators) and does so via a convenient interface. In an experimental comparison with related software, NetworKit shows the best performance on a range of typical analysis tasks.

Connected through hyperedge HE-1 (induced by source paper "combined centrality measures for an improved characterization of influence spread in social networks"):
  with members:
    - "simulating sir processes on networks using weighted shortest paths"
    - "gemsec graph embedding with self clustering"
    - "adaptive influence maximization in dynamic social networks"
    - "kadabra is an adaptive algorithm for betweenness via random approximation"
    - "effector detection in social networks"

Connected through hyperedge HE-2 (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-3 (induced by source paper "group centrality maximization for large scale graphs"):
  with members:
    - "network density of states"
    - "current flow group closeness centrality for complex networks"
    - "pitfalls of graph neural network evaluation"
    - "scalable katz ranking computation in large static and dynamic graphs"
    - "computing top k closeness centrality faster in unweighted graphs"
    - "scaling up group closeness maximization"
    - "generating massive complex networks with hyperbolic geometry faster in practice"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-4 (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"
    - "scalable online betweenness centrality in evolving graphs"
    - "approximating betweenness centrality in large evolving networks"
    - "axioms for centrality"

Connected through hyperedge HE-5 (induced by source paper "jgrapht a java library for graph data structures and algorithms"):
  with members:
    - "graphblast a high performance linear algebra based graph framework on the gpu"
    - "mathematical foundations of the graphblas"
    - "tight lower and upper bounds for the complexity of canonical colour refinement"
    - "gunrock a high performance graph processing library on the gpu"
    - "pagerank beyond the web"
    - "malware classification based on call graph clustering"
    - "listing all maximal cliques in sparse graphs in near optimal time"

Connected through hyperedge HE-6 (induced by source paper "local search for group closeness maximization on big graphs"):
  with members:
    - "computing top k closeness centrality faster in unweighted graphs"
    - "scaling up group closeness maximization"
    - "generating massive complex networks with hyperbolic geometry faster in practice"
    - "powers of tensors and fast matrix multiplication"
    - "axioms for centrality"

Connected through hyperedge HE-7 (induced by source paper "scaling betweenness approximation to billions of edges by mpi based adaptive sampling"):
  with members:
    - "parallel adaptive sampling with almost no synchronization"
    - "snap a general purpose network analysis and graph mining library"
    - "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"
    - "scaling betweenness centrality using communication efficient sparse matrix multiplication"

Connected through hyperedge HE-8 (induced by source paper "parallel adaptive sampling with almost no synchronization"):
  with members:
    - "novel adaptive algorithms for estimating betweenness coverage and k path centralities"
    - "kadabra is an adaptive algorithm for betweenness via random approximation"
    - "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"
    - "axioms for centrality"

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)