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: "local community detection in dynamic networks"
  Abstract: Given a time-evolving network, how can we detect communities over periods of high internal and low external interactions? To address this question we generalize traditional local community detection in graphs to the setting of dynamic networks. Adopting existing static-network approaches in an "aggregated" graph of all temporal interactions is not appropriate for the problem as dynamic communities may be short-lived and thus lost when mixing interactions over long periods. Hence, dynamic community mining requires the detection of both the community nodes and an optimal time interval in which they are actively interacting. #R##N#We propose a filter-and-verify framework for dynamic community detection. To scale to long intervals of graph evolution, we employ novel spectral bounds for dynamic community conductance and employ them to filter suboptimal periods in near-linear time. We also design a time-and-graph-aware locality sensitive hashing family to effectively spot promising community cores. Our method PHASR discovers communities of consistently higher quality (2 to 67 times better) than those of baselines. At the same time, our bounds allow for pruning between $55\%$ and $95\%$ of the search space, resulting in significant savings in running time compared to exhaustive alternatives for even modest time intervals of graph evolution.

Connected through hyperedge HE-1 (induced by source paper "a survey of community search over big graphs"):
  with members:
    - "exploring communities in large profiled graphs"
    - "efficient algorithms for densest subgraph discovery"
    - "community discovery in dynamic networks a survey"
    - "pp dblp modeling and generating attributed public private networks with dblp"
    - "an optimal and progressive approach to online search of top k influential communities"
    - "diversified coherent core search on multi layer graphs"
    - "network community detection a review and visual survey"
    - "local community extraction in directed networks"

Connected through hyperedge HE-2 (induced by source paper "finding events in temporal networks segmentation meets densest subgraph discovery"):
  with members:
    - "strongly polynomial efficient approximation scheme for segmentation"
    - "evidense a graph based method for finding unique high impact events with succinct keyword based descriptions"
    - "best friends forever bff finding lasting dense subgraphs"
    - "densest subgraph in dynamic graph streams"
    - "applications of uniform sampling densest subgraph and beyond"
    - "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"
    - "the bursty dynamics of the twitter information network"
    - "a novel approach to finding near cliques the triangle densest subgraph problem"

Connected through hyperedge HE-3 (induced by source paper "an efficient system for subgraph discovery"):
  with members:
    - "discriminative subnetworks with regularized spectral learning for global state network data"
    - "mining frequent neighborhood patterns in a large labeled graph"
    - "identifying the signs of fraudulent accounts using data mining techniques"
    - "defining and evaluating network communities based on ground truth"
    - "listing all maximal cliques in large sparse real world graphs"
    - "graphlab a new framework for parallel machine learning"
    - "a local algorithm for finding dense subgraphs"

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)