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: "listing all maximal k plexes in temporal graphs"
  Abstract: Many real-world networks evolve over time, that is, new contacts appear and old contacts may disappear. They can be modeled as temporal graphs where interactions between vertices (which represent people in the case of social networks) are represented by time-stamped edges. One of the most fundamental problems in (social) network analysis is community detection, and one of the most basic primitives to model a community is a clique. Addressing the problem of finding communities in temporal networks, Viard et al. [TCS 2016] introduced $\Delta$-cliques as a natural temporal version of cliques. Himmel et al. [SNAM 2017] showed how to adapt the well-known Bron-Kerbosch algorithm to enumerate $\Delta$-cliques. We continue this work and improve and extend the algorithm of Himmel et al. to enumerate temporal $k$-plexes (notably, cliques are the special case $k=1$). #R##N#We define a $\Delta$-$k$-plex as a set of vertices with a lifetime, where during the lifetime each vertex has in each consecutive $\Delta + 1$ time steps edges to all but at most $k-1$ vertices in the chosen set of vertices. We develop a recursive algorithm for enumerating all maximal $\Delta$-k-plexes and perform experiments on real-world social networks that demonstrate the practical feasibility of our approach. In particular, for the special case of $\Delta$-1-plexes (that is, $\Delta$-cliques), we observe that our algorithm is on average significantly faster than the previous algorithms by Himmel et al. [SNAM 2017] and Viard et al. [IPL 2018] for enumerating $\Delta$-cliques.

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"
    - "mining maximal span cores from temporal networks"
    - "to be connected or not to be connected that is the minimum inefficiency subgraph problem"
    - "the complexity of finding small separators in temporal graphs"

Connected through hyperedge HE-2 (induced by source paper "enumerating isolated cliques in temporal networks"):
  with members:
    - "community discovery in dynamic networks a survey"
    - "the parameterized complexity of finding secluded solutions to some classical optimization problems on graphs"
    - "enumerating maximal cliques in link streams with durations"
    - "stream graphs and link streams for the modeling of interactions over time"
    - "adapting the bron kerbosch algorithm for enumerating maximal cliques in temporal graphs"
    - "an introduction to temporal graphs an algorithmic perspective"
    - "computing maximal cliques in link streams"
    - "parameterized complexity of secluded connectivity problems"

Connected through hyperedge HE-3 (induced by source paper "sliding window temporal graph coloring"):
  with members:
    - "deleting edges to restrict the size of an epidemic in temporal networks"
    - "temporal graph classes a view through temporal separators"
    - "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"
    - "an introduction to temporal graphs an algorithmic perspective"
    - "computing maximal cliques in link streams"
    - "on temporal graph exploration"

Connected through hyperedge HE-4 (induced by source paper "the complexity of finding small separators in temporal graphs"):
  with members:
    - "temporal vertex cover with a sliding time window"
    - "sliding window temporal graph coloring"
    - "cluster editing for multi layer and temporal graphs"
    - "stream graphs and link streams for the modeling of interactions over time"
    - "on the size and the approximability of minimum temporally connected subgraphs"
    - "adapting the bron kerbosch algorithm for enumerating maximal cliques in temporal graphs"
    - "survivability in time varying networks"
    - "an introduction to temporal graphs an algorithmic perspective"

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)