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: "beating treewidth for average case subgraph isomorphism"
  Abstract: For any fixed graph $G$, the subgraph isomorphism problem asks whether an $n$-vertex input graph has a subgraph isomorphic to $G$. A well-known algorithm of Alon, Yuster and Zwick (1995) efficiently reduces this to the "colored" version of the problem, denoted $G$-$\mathsf{SUB}$, and then solves $G$-$\mathsf{SUB}$ in time $O(n^{tw(G)+1})$ where $tw(G)$ is the treewidth of $G$. Marx (2010) conjectured that $G$-$\mathsf{SUB}$ requires time $\Omega(n^{\mathrm{const}\cdot tw(G)})$ and, assuming the Exponential Time Hypothesis is true, proved a lower bound of $\Omega(n^{\mathrm{const}\cdot emb(G)})$ for a certain graph parameter $emb(G) = \Omega(tw(G)/\log tw(G))$. With respect to the size of $\mathrm{AC}^0$ circuits solving $G$-$\mathsf{SUB}$, Li, Razborov and Rossman (2017) proved an unconditional average-case lower bound of $\Omega(n^{\kappa(G)})$ for a different graph parameter $\kappa(G) = \Omega(tw(G)/\log tw(G))$. #R##N#Our contributions are as follows. First, we show that $emb(G)$ is at most $O(\kappa(G))$ for all graphs $G$. Next, we show that $\kappa(G)$ can be asymptotically less than $tw(G)$; for example if $G$ is a hypercube, then $\kappa(G)$ is $\Theta\left(tw(G)/\sqrt{\log tw(G)}\right)$. Finally, we construct $\mathrm{AC}^0$ circuits of size $O(n^{\kappa(G)+\mathrm{const}})$ that solve $G$-$\mathsf{SUB}$ in the average case, on a variety of product distributions. This improves an $O(n^{2\kappa(G)+\mathrm{const}})$ upper bound of Li et al, and shows that the average-case complexity of $G$-$\mathsf{SUB}$ is $n^{o(tw(G))}$ for certain families of graphs $G$ such as hypercubes.

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.CC(Computational Complexity)