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: "real time discovery of dense clusters in highly dynamic graphs identifying real world events in highly dynamic environments"
  Abstract: Due to their real time nature, microblog streams are a rich source of dynamic information, for example, about emerging events. Existing techniques for discovering such events from a microblog stream in real time (such as Twitter trending topics), have several lacunae when used for discovering emerging events; extant graph based event detection techniques are not practical in microblog settings due to their complexity; and conventional techniques, which have been developed for blogs, web-pages, etc., involving the use of keyword search, are only useful for finding information about known events. Hence, in this paper, we present techniques to discover events that are unraveling in microblog message streams in real time so that such events can be reported as soon as they occur. We model the problem as discovering dense clusters in highly dynamic graphs. Despite many recent advances in graph analysis, ours is the first technique to identify dense clusters in massive and highly dynamic graphs in real time. Given the characteristics of microblog streams, in order to find clusters without missing any events, we propose and exploit a novel graph property which we call short-cycle property. Our algorithms find these clusters efficiently in spite of rapid changes to the microblog streams. Further we present a novel ranking function to identify the important events. Besides proving the correctness of our algorithms we show their practical utility by evaluating them using real world microblog data. These demonstrate our technique's ability to discover, with high precision and recall, emerging events in high intensity data streams in real time. Many recent web applications create data which can be represented as massive dynamic graphs. Our technique can be easily extended to discover, in real time, interesting patterns in such graphs.

Connected through hyperedge HE-1 (induced by source paper "mining bursting communities in temporal graphs"):
  with members:
    - "distance generalized core decomposition"
    - "community discovery in dynamic networks a survey"
    - "finding events in temporal networks segmentation meets densest subgraph discovery"
    - "mining maximal span cores from temporal networks"
    - "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-2 (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-3 (induced by source paper "topicality and impact in social media diverse messages focused messengers"):
  with members:
    - "can cascades be predicted"
    - "bigbirds never die understanding social dynamics of emergent hashtag"
    - "the role of information diffusion in the evolution of social networks"
    - "clustering memes in social media"
    - "virality prediction and community structure in social networks"
    - "on the interplay between social and topical structure"
    - "folks in folksonomies social link prediction from shared metadata"
    - "influence and passivity in social media"

Connected through hyperedge HE-4 (induced by source paper "stable cluster core detection in correlated hashtag graph"):
  with members:
    - "quantifying social network dynamics"

Connected through hyperedge HE-5 (induced by source paper "clustering evolving networks"):
  with members:
    - "labelrank a stabilized label propagation algorithm for community detection in networks"
    - "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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "clustering memes in social media"):
  with members:
    - "the digital evolution of occupy wall street"
    - "the geospatial characteristics of a social movement communication network"
    - "dynamical classes of collective attention in twitter"
    - "truthy mapping the spread of astroturf in microblog streams"

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)