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: "labelrank a stabilized label propagation algorithm for community detection in networks"
  Abstract: An important challenge in big data analysis nowadays is detection of cohesive groups in large-scale networks, including social networks, genetic networks, communication networks and so. In this paper, we propose LabelRank, an efficient algorithm detecting communities through label propagation. A set of operators is introduced to control and stabilize the propagation dynamics. These operations resolve the randomness issue in traditional label propagation algorithms (LPA), stabilizing the discovered communities in all runs of the same network. Tests on real-world networks demonstrate that LabelRank significantly improves the quality of detected communities compared to LPA, as well as other popular algorithms.

Connected through hyperedge HE-1 (induced by source paper "discovering communities of community discovery"):
  with members:
    - "a survey of community search over big graphs"
    - "community structure a comparative evaluation of community detection methods"
    - "community discovery in dynamic networks a survey"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "clustering and community detection in directed networks a survey"
    - "demon a local first discovery method for overlapping communities"
    - "a classification for community discovery methods in complex networks"
    - "community detection via semi synchronous label propagation algorithms"

Connected through hyperedge HE-2 (induced by source paper "distributed community detection via metastability of the 2 choices dynamics"):
  with members:
    - "pooling or sampling collective dynamics for electrical flow estimation"
    - "phase transition of the 2 choices dynamics on core periphery networks"
    - "the ants problem"
    - "limits for rumor spreading in stochastic populations"
    - "ignore or comply on breaking symmetry in consensus"
    - "distributed graph clustering and sparsification"
    - "average whenever you meet opportunistic protocols for community detection"
    - "a tight analysis of the parallel undecided state dynamics with two colors"

Connected through hyperedge HE-3 (induced by source paper "community discovery in dynamic networks a survey"):
  with members:
    - "stream graphs and link streams for the modeling of interactions over time"
    - "revealing evolutions in dynamical networks"
    - "the ground truth about metadata and community detection in networks"
    - "on the evaluation potential of quality functions in community detection for different contexts"
    - "a framework for the construction of generative models for mesoscale structure in multilayer networks"
    - "generative benchmark models for mesoscale structures in multilayer networks"
    - "enumerating maximal cliques in temporal graphs"
    - "using multiple criteria methods to evaluate community partitions"

Connected through hyperedge HE-4 (induced by source paper "social centrality using network hierarchy and community structure"):
  with members:
    - "centrality measures for networks with community structure"
    - "axioms for centrality"
    - "truss decomposition in massive networks"
    - "k core decomposition of internet graphs hierarchies self similarity and measurement biases"

Connected through hyperedge HE-5 (induced by source paper "olcpm an online framework for detecting overlapping communities in dynamic social networks"):
  with members:
    - "detecting criminal organizations in mobile phone networks"
    - "labelrankt incremental community detection in dynamic networks via label propagation"
    - "community detection using a neighborhood strength driven label propagation algorithm"

Connected through hyperedge HE-6 (induced by source paper "scalable label propagation algorithms for heterogeneous networks"):
  with members:
    - "a survey of heterogeneous information network analysis"
    - "parallel processing of large graphs"

Connected through hyperedge HE-7 (induced by source paper "label propagation for clustering"):
  with members:
    - "corrected overlap weight and clustering coefficient"
    - "the many facets of community detection in complex networks"
    - "clustering scientific publications based on citation relations a systematic comparison of different methods"
    - "identifying robust communities and multi community nodes by combining top down and bottom up approaches to clustering"
    - "on the relationship between gaussian stochastic blockmodels and label propagation algorithms"
    - "group detection in complex networks an algorithm and comparison of the state of the art"
    - "demon a local first discovery method for overlapping communities"
    - "towards linear time overlapping community detection in social networks"

Connected through hyperedge HE-8 (induced by source paper "clustering evolving networks"):
  with members:
    - "benchmarking community detection methods on social media data"
    - "labelrankt incremental community detection in dynamic networks via label propagation"
    - "on graph stream clustering with side information"
    - "real time discovery of dense clusters in highly dynamic graphs identifying real world events in highly dynamic environments"
    - "detect overlapping and hierarchical community structure in networks"
    - "efficient modularity optimization by multistep greedy algorithm and vertex mover refinement"
    - "a tutorial on spectral clustering"
    - "on the np completeness of some graph cluster measures"

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)