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: "modularity in complex multilayer networks with multiple aspects a static perspective"
  Abstract: Complex systems are usually illustrated by networks which captures the topology of the interactions between the entities. To better understand the roles played by the entities in the system one needs to uncover the underlying community structure of the system. In recent years, systems with interactions that have various types or can change over time between the entities have attracted an increasing research attention. However, algorithms aiming to solve the key problem - community detection - in multilayer networks are still limited. In this work, we first introduce the multilayer network model representation with multiple aspects, which is flexible to a variety of networks. Then based on this model, we naturally derive the multilayer modularity - a widely adopted objective function of community detection in networks - from a static perspective as an evaluation metric to evaluate the quality of the communities detected in multilayer networks. It enables us to better understand the essence of the modularity by pointing out the specific kind of communities that will lead to a high modularity score. We also propose a spectral method called mSpec for the optimization of the proposed modularity function based on the supra-adjacency representation of the multilayer networks. Experiments on the electroencephalograph network and the comparison results on several empirical multilayer networks demonstrate the feasibility and reliable performance of the proposed method.

Connected through hyperedge HE-1 (induced by source paper "hyperbolic multiplex network embedding with maps of random walk"):
  with members:
    - "multi relational poincar e graph embeddings"
    - "representation learning for attributed multiplex heterogeneous network"
    - "mvn2vec preservation and collaboration in multi view network embedding"
    - "an attention based collaboration framework for multi view network representation learning"
    - "principled multilayer network embedding"
    - "a survey on network embedding"
    - "poincar e embeddings for learning hierarchical representations"
    - "representation learning for scale free networks"

Connected through hyperedge HE-2 (induced by source paper "structure preserving community in a multilayer network definition detection and analysis"):
  with members:
    - "generic multilayer network data analysis with the fusion of content and structure"
    - "community extraction in multilayer networks with heterogeneous community structure"
    - "organizational chart inference"
    - "a survey of heterogeneous information network analysis"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "querying knowledge graphs by example entity tuples"
    - "mining attribute structure correlated patterns in large attributed graphs"
    - "clustering with multi layer graphs a spectral perspective"

Connected through hyperedge HE-3 (induced by source paper "efficient community detection in boolean composed multiplex networks"):
  with members:
    - "community detection link prediction and layer interdependence in multilayer networks"
    - "generalized measures for the evaluation of community detection methods"
    - "networkit an interactive tool suite for high performance network analysis"
    - "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-4 (induced by source paper "modularity in multilayer networks using redundancy based resolution and projection based inter layer coupling"):
  with members:
    - "consensus community detection in multilayer networks using parameter free graph pruning"
    - "revisiting resolution and inter layer coupling factors in modularity for multilayer networks"
    - "clustering network layers with the strata multilayer stochastic block model"
    - "community detection in multiplex networks using locally adaptive random walks"
    - "a classification for community discovery methods in complex networks"

Connected through hyperedge HE-5 (induced by source paper "revisiting resolution and inter layer coupling factors in modularity for multilayer networks"):
  with members:
    - "community detection in multiplex networks using locally adaptive random walks"

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)