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: "parameterized objectives and algorithms for clustering bipartite graphs and hypergraphs"
  Abstract: Graph clustering objective functions with tunable resolution parameters make it possible to detect different types of clustering structure in the same graph. These objectives also provide a unifying view of other non-parametric objectives, which often can be captured as special cases. Previous research has largely focused on parametric objectives for standard graphs, in which all nodes are of the same type, and edges model pairwise relationships. In our work, we introduced parameterized objective functions and approximation algorithms specifically for clustering bipartite graphs and hypergraphs, based on correlation clustering. This enables us to develop principled approaches for clustering datasets with different node types (bipartite graphs) or multiway relationships (hypergraphs). Our hypergraph objective is related to higher-order notions of modularity and normalized cut, and is amenable to approximation algorithms via hypergraph expansion techniques. Our bipartite objective generalizes standard bipartite correlation clustering, and in a certain parameter regime is equivalent to bicluster deletion, i.e., removing a minimum number of edges to separate a bipartite graph into disjoint bicliques. The problem in general is NP-hard, but we show that in a certain parameter regime it is equivalent to a bipartite matching problem, meaning that it is polynomial time solvable in this regime. For other regimes, we provide approximation guarantees based on LP-rounding. Our results include the first constant factor approximation algorithm for bicluster deletion. We illustrate the flexibility of our framework in several experiments. This includes clustering a food web and an email network based on higher-order motif structure, detecting clusters of retail products in product review hypergraph, and evaluating our algorithms across a range of parameter settings on several real world bipartite graphs.

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.DS(Data Structures and Algorithms)