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: "on the complexity and approximation of lambda_ infty spread constant and maximum variance embedding"
  Abstract: Twenty years ago, Bobkov, Houdr\'e, and the last author introduced a Poincar\'e-type functional graph parameter, $\lambda_\infty(G)$, of a graph $G$, and related it to the {\em vertex expansion} of $G$ via a Cheeger-type inequality. This is analogous to the Cheeger-type inequality relating the spectral gap, $\lambda_2(G)$, of the graph to its {\em edge expansion}.  While $\lambda_2$ can be computed efficiently, the computational complexity of $\lambda_\infty$ has remained an open question. Following the work of the second author with Raghavendra and Vempala, wherein the complexity of $\lambda_\infty$ was related to the so-called Small-Set Expansion (SSE) problem, it has been believed that computing $\lambda_\infty$ is a hard problem. We settle this question by proving that computing $\lambda_\infty$ is indeed NP-hard. Additionally, we use our techniques to prove NP-hardness of computing the spread constant (of a graph), a geometric measure introduced by Alon, Boppana, and Spencer, in the context of deriving an asymptotic isoperimetric inequality on cartesian products of graphs.  We complement our hardness results by providing approximation schemes for computing $\lambda_\infty$ and the spread constant of star graphs, and investigate constant approximability for weighted trees.  Finally, we provide improved approximation results for the maximum variance embedding problem for general graphs, by replacing the optimal orthogonal projection (PCA) with a randomized projection approach.

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)