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: "from louvain to leiden guaranteeing well connected communities"
  Abstract: Community detection is often used to understand the structure of large and complex networks. One of the most popular algorithms for uncovering community structure is the so-called Louvain algorithm. We show that this algorithm has a major defect that largely went unnoticed until now: the Louvain algorithm may yield arbitrarily badly connected communities. In the worst case, communities may even be disconnected, especially when running the algorithm iteratively. In our experimental analysis, we observe that up to 25% of the communities are badly connected and up to 16% are disconnected. To address this problem, we introduce the Leiden algorithm. We prove that the Leiden algorithm yields communities that are guaranteed to be connected. In addition, we prove that, when the Leiden algorithm is applied iteratively, it converges to a partition in which all subsets of all communities are locally optimally assigned. Furthermore, by relying on a fast local move approach, the Leiden algorithm runs faster than the Louvain algorithm. We demonstrate the performance of the Leiden algorithm for several benchmark and real-world networks. We find that the Leiden algorithm is faster than the Louvain algorithm and uncovers better partitions, in addition to providing explicit guarantees.

Connected through hyperedge HE-1 (induced by source paper "shapevis high dimensional data visualization at scale"):
  with members:
    - "statistical analysis and parameter selection for mapper"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "line large scale information network embedding"
    - "distributed representations of words and phrases and their compositionality"
    - "representation learning a review and new perspectives"
    - "defining and evaluating network communities based on ground truth"
    - "generalized louvain method for community detection in large networks"

Connected through hyperedge HE-2 (induced by source paper "modularity deltas a semi global centrality measure for complex networks"):
  with members:
    - "ga based q attack on community detection"

Connected through hyperedge HE-3 (induced by source paper "a complex network approach to political analysis application to the brazilian chamber of deputies"):
  with members:
    - "word sense disambiguation a complex network approach"
    - "extractive multi document summarization using multilayer networks"
    - "analyzing dynamic ideological communities in congressional voting networks"
    - "patterns of authors contribution in scientific manuscripts"
    - "knowledge acquisition a complex networks approach"
    - "using network science and text analytics to produce surveys in a scientific topic"
    - "using complex networks for text classification discriminating informative and imaginative documents"
    - "cohesion and coalition formation in the european parliament roll call votes and twitter activities"

Connected through hyperedge HE-4 (induced by source paper "modeling graphs with vertex replacement grammars"):
  with members:
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "graph embedding techniques applications and performance a survey"
    - "learning hyperedge replacement grammars for graph generation"
    - "node2vec scalable feature learning for networks"
    - "growing graphs from hyperedge replacement graph grammars"
    - "an empirical comparison of the summarization power of graph clustering methods"
    - "line large scale information network embedding"

Connected through hyperedge HE-5 (induced by source paper "viewing computer science through citation analysis salton and bergmark redux"):
  with members:
    - "which type of citation analysis generates the most accurate taxonomy of scientific and technical knowledge"
    - "role of interdisciplinarity in computer sciences quantification impact and life trajectory"
    - "parallel local graph clustering"
    - "clustering scientific publications based on citation relations a systematic comparison of different methods"
    - "analysis of network clustering algorithms and cluster quality metrics at scale"
    - "large scale analysis of the accuracy of the journal classification systems of web of science and scopus"
    - "a new methodology for constructing a publication level classification system of science"

Connected through hyperedge HE-6 (induced by source paper "how to interpret algorithmically constructed topical structures of research specialties a case study comparing an internal and an external mapping of the topical structure of invasion biology"):
  with members:
    - "granularity of algorithmically constructed publication level classifications of research publications identification of topics"
    - "algorithmically generated subject categories based on citation relations an empirical micro study using papers on overall water splitting"
    - "which type of citation analysis generates the most accurate taxonomy of scientific and technical knowledge"
    - "mutual information based labelling and comparing clusters"
    - "memetic search for overlapping topics based on a local evaluation of link communities"
    - "clustering scientific publications based on citation relations a systematic comparison of different methods"
    - "a new methodology for constructing a publication level classification system of science"

Connected through hyperedge HE-7 (induced by source paper "a principled methodology for comparing relatedness measures for clustering publications"):
  with members:
    - "granularity of algorithmically constructed publication level classifications of research publications identification of topics"
    - "algorithmically generated subject categories based on citation relations an empirical micro study using papers on overall water splitting"
    - "which type of citation analysis generates the most accurate taxonomy of scientific and technical knowledge"
    - "constructing bibliometric networks a comparison between full and fractional counting"
    - "clustering scientific publications based on citation relations a systematic comparison of different methods"
    - "citnetexplorer a new software tool for analyzing and visualizing citation networks"
    - "a new methodology for constructing a publication level classification system of science"

Connected through hyperedge HE-8 (induced by source paper "co citations in context disciplinary heterogeneity is relevant"):
  with members:
    - "which type of citation analysis generates the most accurate taxonomy of scientific and technical knowledge"
    - "research portfolio analysis and topic prominence"
    - "citing for high impact"
    - "measuring contextual citation impact of scientific journals"

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)