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: "distributed detection of cliques in dynamic networks"
  Abstract: This paper provides an in-depth study of the fundamental problems of finding small subgraphs in distributed dynamic networks. While some problems are trivially easy to handle, such as detecting a triangle that emerges after an edge insertion, we show that, perhaps somewhat surprisingly, other problems exhibit a wide range of complexities in terms of the trade-offs between their round and bandwidth complexities. In the case of triangles, which are only affected by the topology of the immediate neighborhood, some end results are: #R##N#\begin{itemize} #R##N#\item The bandwidth complexity of $1$-round dynamic triangle detection or listing is $\Theta(1)$. #R##N#\item The bandwidth complexity of $1$-round dynamic triangle membership listing is $\Theta(1)$ for node/edge deletions, $\Theta(n^{1/2})$ for edge insertions, and $\Theta(n)$ for node insertions. #R##N#\item The bandwidth complexity of $1$-round dynamic triangle membership detection is $\Theta(1)$ for node/edge deletions, $O(\log n)$ for edge insertions, and $\Theta(n)$ for node insertions. #R##N#\end{itemize} #R##N#Most of our upper and lower bounds are \emph{tight}. Additionally, we provide almost always tight upper and lower bounds for larger cliques.

Connected through hyperedge HE-1 (induced by source paper "distributed testing of graph isomorphism in the congest model"):
  with members:
    - "property testing of planarity in the congest model"
    - "distributed triangle detection via expander decomposition"
    - "detecting cliques in congest networks"
    - "distributed subgraph detection"
    - "fooling views a new lower bound technique for distributed computations under congestion"
    - "distributed detection of cycles"
    - "faster and simpler distributed algorithms for testing and correcting graph properties in the congest model"
    - "triangle finding and listing in congest 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.DC(Distributed, Parallel, and Cluster Computing)