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: "faster subgraph counting in sparse graphs"
  Abstract: A fundamental graph problem asks to compute the number of induced copies of a $k$-node pattern graph $H$ in an $n$-node graph $G$. The fastest algorithm to date is still the 35-years-old algorithm by Ne\v{s}et\v{r}il and Poljak [31], with running time $f(k) \cdot O(n^{\omega\lfloor\frac{k}{3}\rfloor + 2})$ where $\omega \le 2.373$ is the matrix multiplication exponent. In this work we show that, if one takes into account the degeneracy $d$ of $G$, then the picture becomes substantially richer and leads to faster algorithms when $G$ is sufficiently sparse. More precisely, after introducing a novel notion of graph width, the \emph{DAG-treewidth}, we prove what follows. If $H$ has DAG-treewidth $\tau(H)$ and $G$ has degeneracy $d$, then the induced copies of $H$ in $G$ can be counted in time $f(d,k) \cdot \tilde{O}(n^{\tau(H)})$; and, under the Exponential Time Hypothesis, no algorithm can solve the problem in time $f(d,k) \cdot n^{o(\tau(H)/\ln \tau(H))}$ for all $H$. This result characterises the complexity of counting subgraphs in a $d$-degenerate graph. Developing bounds on $\tau(H)$, then, we obtain natural generalisations of classic results and faster algorithms for sparse graphs. For example, when $d=O(\operatorname{poly}\log(n))$ we can count the induced copies of any $H$ in time $f(k) \cdot\tilde{O}(n^{\lfloor \frac{k}{4} \rfloor + 2})$, beating the Ne\v{s}et\v{r}il-Poljak algorithm by essentially a cubic factor in $n$.

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)