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: "min morse approximability applications"
  Abstract: We resolve an open problem posed by Joswig et al. by providing an $\tilde{O}(N)$ time, $O(\log^2(N))$-factor approximation algorithm for the min-Morse unmatched problem (MMUP) Let $\Lambda$ be the no. of critical cells of the optimal discrete Morse function and $N$ be the total no. of cells of a regular cell complex K. The goal of MMUP is to find $\Lambda$ for a given complex K. To begin with, we apply an approx. preserving graph reduction on MMUP to obtain a new problem namely the min-partial order problem (min-POP)(a strict generalization of the min-feedback arc set problem). The reduction involves introduction of rigid edges which are edges that demand strict inclusion in output solution. To solve min-POP, we use the Leighton- Rao divide-&-conquer paradigm that provides solutions to SDP-formulated instances of min-directed balanced cut with rigid edges (min-DBCRE). Our first algorithm for min-DBCRE extends Agarwal et al.'s rounding procedure for digraph formulation of ARV-algorithm to handle rigid edges. Our second algorithm to solve min-DBCRE SDP, adapts Arora et al.'s primal dual MWUM. In terms of applications, under the mild assumption1 of the size of topological features being significantly smaller compared to the size of the complex, we obtain an (a) $\tilde{O}(N)$ algorithm for computing homology groups $H_i(K,A)$ of a simplicial complex K, (where A is an arbitrary Abelian group.) (b) an $\tilde{O}(N^2)$ algorithm for computing persistent homology and (c) an $\tilde{O}(N)$ algorithm for computing the optimal discrete Morse-Witten function compatible with input scalar function as simple consequences of our approximation algorithm for MMUP thereby giving us the best known complexity bounds for each of these applications under the aforementioned assumption. Such an assumption is realistic in applied settings, and often a characteristic of modern massive datasets.

Connected through hyperedge HE-1 (induced by source paper "a new matching algorithm for multidimensional persistence"):
  with members:
    - "reducing complexes in multidimensional persistent homology theory"

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.CG(Computational Geometry)