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: "a general framework for multi level subsetwise graph sparsifiers"
  Abstract: Given an undirected weighted graph $G(V,E)$, a subsetwise sparsifier over a terminal set $T\subset V$ is a subgraph $G'$ having a certain structure which connects the terminals. Examples are Steiner trees (minimal-weight trees spanning $T$) and subsetwise spanners (subgraphs $G'(V',E')$ such that for given $\alpha,\beta\geq1$, $d_{G'}(u,v)\leq \alpha d_{G}(u,v)+\beta$ for $u,v\in T$). Multi-level subsetwise sparsifiers are generalizations in which terminal vertices require different levels or grades of service. #R##N#This paper gives a flexible approximation algorithm for several multi-level subsetwise sparsifier problems, including multi-level graph spanners, Steiner trees, and $k$--connected subgraphs. The algorithm relies on computing an approximation to the single level instance of the problem% and an efficient approach to obtain a multi-level solution. #R##N#For the subsetwise spanner problem, there are few existing approximation algorithms for even a single level; consequently we give a new polynomial time algorithm for computing a subsetwise spanner for a single level. Specifically, we show that for $k\in\N$, $\eps>0$, and $T\subset V$, there is a subsetwise $(2k-1)(1+\eps)$--spanner with total weight $O(|T|^\frac1kW(\ST(G,T)))$, where $W(\ST(G,T))$ is the weight of the Steiner tree of $G$ over the subset $T$. This is the first algorithm and corresponding weight guarantee for a multiplicative subsetwise spanner for nonplanar graphs. #R##N#We also generalize a result of Klein to give a constant approximation to the multi-level subsetwise spanner problem for planar graphs. Additionally, we give a polynomial-size ILP for optimally computing pairwise spanners of arbitrary distortion (beyond linear distortion functions), and provide experiments to illustrate the performance of our algorithms.

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.DM(Discrete Mathematics)