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: "enumerating maximal cliques in temporal graphs"
  Abstract: Dynamics of interactions play an increasingly important role in the analysis of complex networks. A modeling framework to capture this are temporal graphs. We focus on enumerating delta-cliques, an extension of the concept of cliques to temporal graphs: for a given time period delta, a delta-clique in a temporal graph is a set of vertices and a time interval such that all vertices interact with each other at least after every delta time steps within the time interval. Viard, Latapy, and Magnien [ASONAM 2015] proposed a greedy algorithm for enumerating all maximal delta-cliques in temporal graphs. In contrast to this approach, we adapt to the temporal setting the Bron-Kerbosch algorithm - an efficient, recursive backtracking algorithm which enumerates all maximal cliques in static graphs. We obtain encouraging results both in theory (concerning worst-case time analysis based on the parameter "delta-slice degeneracy" of the underlying graph) as well as in practice with experiments on real-world data. The latter culminates in a significant improvement for most interesting delta-values concerning running time in comparison with the algorithm of Viard, Latapy, and Magnien (typically two orders of magnitude).

Connected through hyperedge HE-1 (induced by source paper "span core decomposition for temporal networks algorithms and applications"):
  with members:
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "a survey of community search over big graphs"
    - "finding events in temporal networks segmentation meets densest subgraph discovery"
    - "graph embedding techniques applications and performance a survey"
    - "on finding dense common subgraphs"
    - "listing all maximal k plexes in temporal graphs"
    - "mining maximal span cores from temporal networks"
    - "to be connected or not to be connected that is the minimum inefficiency subgraph problem"

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"
    - "using multiple criteria methods to evaluate community partitions"
    - "predicting community evolution in social networks"

Connected through hyperedge HE-3 (induced by source paper "listing all maximal k plexes in temporal graphs"):
  with members:
    - "computing maximum matchings in temporal graphs"
    - "temporal vertex cover with a sliding time window"
    - "sliding window temporal graph coloring"
    - "enumerating maximal cliques in link streams with durations"
    - "cluster editing for multi layer and temporal graphs"
    - "stream graphs and link streams for the modeling of interactions over time"
    - "the complexity of finding small separators in temporal graphs"
    - "adapting the bron kerbosch algorithm for enumerating maximal cliques in temporal graphs"

Connected through hyperedge HE-4 (induced by source paper "mining maximal span cores from temporal networks"):
  with members:
    - "best friends forever bff finding lasting dense subgraphs"
    - "computing maximal cliques in link streams"
    - "social resilience in online communities the autopsy of friendster"
    - "prediction of emerging technologies based on analysis of the us patent citation network"
    - "dense subgraph maintenance under streaming edge weight updates for real time story identification"
    - "listing all maximal cliques in sparse graphs in near optimal time"

Connected through hyperedge HE-5 (induced by source paper "on the enumeration of maximal delta gamma cliques of a temporal network"):
  with members:
    - "computing maximal cliques in link streams"
    - "exploring the structure and function of temporal networks with dynamic graphlets"
    - "mining maximal cliques from an uncertain graph"
    - "listing all maximal cliques in large sparse real world graphs"

Connected through hyperedge HE-6 (induced by source paper "movie rating prediction using content based and link stream features"):
  with members:
    - "stream graphs and link streams for the modeling of interactions over time"
    - "recsys challenge 2016 job recommendations based on preselection of offers and gradient boosting"
    - "an algebraic approach to temporal network analysis based on temporal quantities"

Connected through hyperedge HE-7 (induced by source paper "enumerating maximal cliques in link streams with durations"):
  with members:
    - "discovering patterns of interest in ip traffic using cliques in bipartite link streams"
    - "adapting the bron kerbosch algorithm for enumerating maximal cliques in temporal graphs"
    - "computing maximal cliques in link streams"
    - "listing all maximal cliques in large sparse real world graphs"

Connected through hyperedge HE-8 (induced by source paper "discovering patterns of interest in ip traffic using cliques in bipartite link streams"):
  with members:
    - "non altering time scales for aggregation of dynamic networks into series of graphs"
    - "stream graphs and link streams for the modeling of interactions over time"
    - "computing maximal cliques in link streams"
    - "a unifying model for representing time varying graphs"

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.DS(Data Structures and Algorithms)