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: "interpreting communities based on the evolution of a dynamic attributed network"
  Abstract: Many methods have been proposed to detect communities, not only in plain, but also in attributed, di- rected, or even dynamic complex networks. From the modeling point of view, to be of some utility, the com- munity structure must be characterized relatively to the properties of the studied system. However, most of the existing works focus on the detection of communities, and only very few try to tackle this interpretation problem. Moreover, the existing approaches are limited either by the type of data they handle or by the nature of the results they output. In this work, we see the interpretation of commu- nities as a problem independent from the detection process, consisting in identifying the most characteristic features of communities. We give a formal definition of this problem and propose a method to solve it. To this aim, we first define a sequence-based representation of networks, com- bining temporal information, community structure, topo- logical measures, and nodal attributes. We then describe how to identify the most emerging sequential patterns of this dataset and use them to characterize the communities. We study the performance of our method on artificially generated dynamic attributed networks. We also em- pirically validate our framework on real-world systems: a DBLP network of scientific collaborations, and a LastFM network of social and musical interactions.

Connected through hyperedge HE-1 (induced by source paper "change detection in dynamic attributed networks"):
  with members:
    - "anomaly detection in online social networks"
    - "link prediction in networks with nodes attributes by similarity propagation"
    - "detecting change points in the large scale structure of evolving networks"
    - "graph based anomaly detection and description a survey"
    - "large scale spectral clustering using resistance distance and spielman teng solvers"

Connected through hyperedge HE-2 (induced by source paper "the vulnerability of communities in complex network an entropy approach"):
  with members:
    - "measuring network resilience through connection patterns"
    - "identifying influential nodes based on fuzzy local dimension in complex networks"
    - "exploring the evolution of node neighborhoods in dynamic networks"

Connected through hyperedge HE-3 (induced by source paper "community structure characterization"):
  with members:
    - "from community detection to community profiling"
    - "on the evaluation potential of quality functions in community detection for different contexts"
    - "structure and inference in annotated networks"
    - "clustering attributed graphs models measures and methods"
    - "a community role approach to assess social capitalists visibility in the twitter network"
    - "ged the method for group evolution discovery in social networks"
    - "community detection in networks with node attributes"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-4 (induced by source paper "exploring the evolution of node neighborhoods in dynamic networks"):
  with members:
    - "a method for characterizing communities in dynamic attributed complex networks"
    - "role dynamics fast mining of large dynamic networks"
    - "centrality metric for dynamic networks"

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)