human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "many to many correspondences between partitions introducing a cut based approach"):
  Abstract: Let $\mathcal{P}$ and $\mathcal{P}'$ be finite partitions of the set $V$. Finding good correspondences between the parts of $\mathcal{P}$ and those of $\mathcal{P}'$ is helpful in classification, pattern recognition, and network analysis. Unlike common similarity measures for partitions that yield only a single value, we provide specifics on how $\mathcal{P}$ and $\mathcal{P'}$ correspond to each other. #R##N#To this end, we first define natural collections of best correspondences under three constraints \cone, \ctwo, and \cthree. In case of \cone, the best correspondences form a minimum cut basis of a certain bipartite graph, whereas the other two lead to minimum cut bases of $\mathcal{P}$ \wrt $\mathcal{P}'$. We also introduce a constraint, \cfour, which tightens \cthree; both are useful for finding consensus partitions. We then develop branch-and-bound algorithms for finding minimum $P_s$-$P_t$ cuts of $\mathcal{P}$ and thus $\vert \mathcal{P} \vert -1$ best correspondences under \ctwo, \cthree, and \cfour, respectively. #R##N#In a case study, we use the correspondences to gain insight into a community detection algorithm. The results suggest, among others, that only very minor losses in the quality of the correspondences occur if the branch-and-bound algorithm is restricted to its greedy core. Thus, even for graphs with more than half a million nodes and hundreds of communities, we can find hundreds of best or almost best correspondences in less than a minute.

Includes members:
  Member "engineering parallel algorithms for community detection in massive networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "high quality shared memory graph partitioning")
  Member "networkit an interactive tool suite for high performance network analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient community detection in boolean composed multiplex networks")
  Member "networkit a tool suite for large scale complex network analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "combined centrality measures for an improved characterization of influence spread in social networks")
  Member "efficient minimization of decomposable submodular functions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "localized flow based clustering in hypergraphs")
  Member "bipartite graph partitioning and data clustering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "clustering and classification in text collections using graph modularity")
  Member "objective criteria for the evaluation of clustering methods":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "interpretable and fair comparison of link prediction or entity alignment methods with adjusted mean rank")

Question: Please tell me which class the source paper 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)