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: "mining attribute structure correlated patterns in large attributed graphs"
  Abstract: In this work, we study the correlation between attribute sets and the occurrence of dense subgraphs in large attributed graphs, a task we call structural correlation pattern mining. A structural correlation pattern is a dense subgraph induced by a particular attribute set. Existing methods are not able to extract relevant knowledge regarding how vertex attributes interact with dense subgraphs. Structural correlation pattern mining combines aspects of frequent itemset and quasi-clique mining problems. We propose statistical significance measures that compare the structural correlation of attribute sets against their expected values using null models. Moreover, we evaluate the interestingness of structural correlation patterns in terms of size and density. An efficient algorithm that combines search and pruning strategies in the identification of the most relevant structural correlation patterns is presented. We apply our method for the analysis of three real-world attributed graphs: a collaboration, a music, and a citation network, verifying that it provides valuable knowledge in a feasible time.

Connected through hyperedge HE-1 (induced by source paper "detection of community structures in networks with nodal features based on generative probabilistic approach"):
  with members:
    - "iedc an integrated approach for overlapping and non overlapping community detection"
    - "the ground truth about metadata and community detection in networks"
    - "structure and inference in annotated networks"
    - "overlapping community detection using neighborhood inflated seed expansion"
    - "general optimization technique for high quality community detection in complex networks"
    - "community detection in networks with node attributes"
    - "relation strength aware clustering of heterogeneous information networks with incomplete attributes"
    - "social structure of facebook 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"
    - "modularity in complex multilayer networks with multiple aspects a static perspective"
    - "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"
    - "clustering with multi layer graphs a spectral perspective"

Connected through hyperedge HE-3 (induced by source paper "community detection in multiplex networks"):
  with members:
    - "overlapping community detection by local decentralised vertex centred process"
    - "node centric community detection in multilayer networks with layer coverage diversification bias"
    - "mapping higher order network flows in memory and multilayer networks with infomap"
    - "clustering attributed graphs models measures and methods"
    - "community detection in multiplex networks using locally adaptive random walks"
    - "a local perspective on community structure in multilayer networks"
    - "clustering on multi layer graphs via subspace analysis on grassmann manifolds"
    - "efficient community detection in large networks using content and links"

Connected through hyperedge HE-4 (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"
    - "networkit an interactive tool suite for high performance network analysis"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "clustering with multi layer graphs a spectral perspective"

Connected through hyperedge HE-5 (induced by source paper "mining subjectively interesting attributed subgraphs"):
  with members:
    - "a comprehensive survey of graph embedding problems techniques and applications"

Connected through hyperedge HE-6 (induced by source paper "contextual stochastic block models"):
  with members:
    - "algorithmic decision making and the cost of fairness"
    - "clustering attributed graphs models measures and methods"
    - "phase transitions in semisupervised clustering of sparse networks"
    - "statistical estimation from denoising to sparse regression and hidden cliques"
    - "partitioning networks with node attributes by compressing information flow"
    - "community detection in networks with node attributes"
    - "latent multi group membership graph model"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-7 (induced by source paper "enumerating top k quasi cliques"):
  with members:
    - "change sensitive algorithms for maintaining maximal cliques in a dynamic graph"
    - "enumerating maximal bicliques from a large graph using mapreduce"
    - "a novel approach to finding near cliques the triangle densest subgraph problem"
    - "listing all maximal cliques in sparse graphs in near optimal time"
    - "an o m algorithm for cores decomposition of networks"

Connected through hyperedge HE-8 (induced by source paper "mr simlab scalable subgraph selection with label similarity for big data"):
  with members:
    - "protnn fast and accurate protein 3d structure classification in structural and topological space"
    - "big graph mining frameworks and techniques"
    - "density based data partitioning strategy to approximate large scale subgraph mining"
    - "densest subgraph in streaming and mapreduce"

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.DB(Databases)