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 "correspondences between partitions"):
  Abstract: Measures for the overall similarity of partitions $\mathcal{P}=\{P_1,\dots,P_{\vert\mathcal{P}\vert}\}$ and $\mathcal{P}'=\{P'_1,\dots,P'_{\vert\mathcal{P}'\vert}\}$ of the same set $V$ do not provide specifics on how $\mathcal{P}$ and $\mathcal{P}'$ correspond, e.g., when the union of two parts in $\mathcal{P}$ is similar to that of three parts in $\mathcal{P}'$. #R##N#We think of a good correspondence between $\mathcal{P}$ and $\mathcal{P}'$ as a pair $(\mathcal{S},\mathcal{S}')$ with $\mathcal{S}\subseteq\mathcal{P}$, $\mathcal{S}'\subseteq \mathcal{P}'$ and a low value of $\vert(\bigcup_{S\in\mathcal{S}}S)\triangle(\bigcup_{S'\in\mathcal{S}'}S')\vert$, where $\triangle$ and $\vert\cdot\vert$ denote symmetric difference and cardinality [total weight] if the elements of $V$ are unweighted [weighted]. Such an objective function is essentially one for $\mathcal{S}\subseteq\mathcal{P}$ only and yields the weight of the cut $(\mathcal{S},\mathcal{P} \setminus \mathcal{S})$ w.r.t. $\mathcal{P}'$}. #R##N#Thus, we can derive a hierarchy of the $\vert\mathcal{P}-1\vert$ best correspondences from a minimal cut basis of $\mathcal{P}$. A minimal $P_s$-$P_t$ cut can be computed in time $O(\vert V\vert+\vert\mathcal{P}\vert^4\vert\mathcal{P}'\vert)$. #R##N#To reduce the running time in practice, we present a branch-and-bound algorithm for finding minimal $P_s$-$P_t$ cuts. When the entire cut basis needs to be computed, the algorithm is feasible up to $\vert\mathcal{P}\vert,\vert\mathcal{P}'\vert$ around 1000. The number of elements in $V$ can be neglected. #R##N#Finally, we use correspondences to gain insight into a parallel community detection algorithm that is non-deterministic due to race conditions and that has an optional refinement phase. It turns out that the race conditions frequently entail unions or break-ups of communities, while the refinement leaves most communities intact.

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 "efficient minimization of decomposable submodular functions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "localized flow based clustering in hypergraphs")
  Member "objective criteria for the evaluation of clustering methods":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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)