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: "enhancing community detection using a network weighting strategy"
  Abstract: A community within a network is a group of vertices densely connected to each other but less connected to the vertices outside. The problem of detecting communities in large networks plays a key role in a wide range of research areas, e.g. Computer Science, Biology and Sociology. Most of the existing algorithms to find communities count on the topological features of the network and often do not scale well on large, real-life instances. In this article we propose a strategy to enhance existing community detection algorithms by adding a pre-processing step in which edges are weighted according to their centrality, w.r.t. the network topology. In our approach, the centrality of an edge reflects its contribute to making arbitrary graph transversals, i.e., spreading messages over the network, as short as possible. Our strategy is able to effectively complements information about network topology and it can be used as an additional tool to enhance community detection. The computation of edge centralities is carried out by performing multiple random walks of bounded length on the network. Our method makes the computation of edge centralities feasible also on large-scale networks. It has been tested in conjunction with three state-of-the-art community detection algorithms, namely the Louvain method, COPRA and OSLOM. Experimental results show that our method raises the accuracy of existing algorithms both on synthetic and real-life datasets.

Connected through hyperedge HE-1 (induced by source paper "an ensemble based on a bi objective evolutionary spectral algorithm for graph clustering"):
  with members:
    - "detecting criminal organizations in mobile phone networks"
    - "spectral redemption in clustering sparse networks"

Connected through hyperedge HE-2 (induced by source paper "adversarial enhancement for community detection in complex networks"):
  with members:
    - "edmot an edge enhancement approach for motif aware community detection"
    - "adversarial attack on graph structured data"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "ga based q attack on community detection"
    - "target defense against link prediction based attacks via evolutionary perturbations"
    - "hiding individuals and communities in a social network"
    - "node2vec scalable feature learning for networks"
    - "efficient and principled method for detecting communities in networks"

Connected through hyperedge HE-3 (induced by source paper "sorec a social relation based centrality measure in mobile social networks"):
  with members:
    - "d2d big data content deliveries over wireless device to device sharing in large scale mobile networks"
    - "mixing local and global information for community detection in large networks"
    - "a novel measure of edge centrality in social networks"

Connected through hyperedge HE-4 (induced by source paper "a new method for quantifying network cyclic structure to improve community detection"):
  with members:
    - "network clustering and community detection using modulus of families of loops"

Connected through hyperedge HE-5 (induced by source paper "adaptive modularity maximization via edge weighting scheme"):
  with members:
    - "the ground truth about metadata and community detection in networks"
    - "a new metric for quality of network community structure"
    - "efficient community detection in large networks using content and links"
    - "defining and evaluating network communities based on ground truth"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-6 (induced by source paper "new methods for incorporating network cyclic structures to improve community detection"):
  with members:
    - "network clustering and community detection using modulus of families of loops"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "using triangles to improve community detection in directed networks"

Connected through hyperedge HE-7 (induced by source paper "ensemble based discovery of disjoint overlapping and fuzzy community structures in networks"):
  with members:
    - "ensemble based algorithms to detect disjoint and overlapping communities in networks"
    - "permanence and community structure in complex networks"
    - "metrics for community analysis a survey"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "personalized pagerank solution paths"
    - "leveraging disjoint communities for detecting overlapping community structure"
    - "yasca a collective intelligence approach for community detection in complex networks"
    - "overlapping community detection in networks the state of the art and comparative study"

Connected through hyperedge HE-8 (induced by source paper "permanence and community structure in complex networks"):
  with members:
    - "genperm a unified method for detecting non overlapping and overlapping communities"
    - "a new metric for quality of network community structure"
    - "leveraging disjoint communities for detecting overlapping community structure"
    - "community detection and graph partitioning"
    - "discovering link communities in complex networks by exploiting link dynamics"
    - "towards linear time overlapping community detection in social networks"
    - "comparative evaluation of community detection algorithms a topological approach"
    - "defining and evaluating network communities based on ground truth"

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)