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: "approximating multicut and the demand graph"
  Abstract: In the minimum Multicut problem, the input is an edge-weighted supply graph $G=(V,E)$ and a simple demand graph $H=(V,F)$. Either $G$ and $H$ are directed (DMulC) or both are undirected (UMulC). The goal is to remove a minimum weight set of edges in $G$ such that there is no path from $s$ to $t$ in the remaining graph for any $(s,t) \in F$. UMulC admits an $O(\log k)$-approximation where $k$ is the vertex cover size of $H$ while the best known approximation for DMulC is $\min\{k, \tilde{O}(n^{11/23})\}$. These approximations are obtained by proving corresponding results on the multicommodity flow-cut gap. In contrast to these results some special cases of Multicut, such as the well-studied Multiway Cut problem, admit a constant factor approximation in both undirected and directed graphs. Motivated by both concrete instances from applications and abstract considerations, we consider the role that the structure of the demand graph $H$ plays in determining the approximability of Multicut. #R##N#In undirected graphs our main result is a $2$-approximation in $n^{O(t)}$ time when the demand graph $H$ excludes an induced matching of size $t$. This gives a constant factor approximation for a specific demand graph that motivated this work. #R##N#In contrast to undirected graphs, we prove that in directed graphs such approximation algorithms can not exist. Assuming the Unique Games Conjecture (UGC), for a large class of fixed demand graphs DMulC cannot be approximated to a factor better than worst-case flow-cut gap. As a consequence we prove that for any fixed $k$, assuming UGC, DMulC with $k$ demand pairs is hard to approximate to within a factor better than $k$. On the positive side, we prove an approximation of $k$ when the demand graph excludes certain graphs as an induced subgraph. This generalizes the Multiway Cut result to a much larger class of demand graphs.

Connected through hyperedge HE-1 (induced by source paper "a near linear approximation scheme for multicuts of embedded graphs with a fixed number of terminals"):
  with members:
    - "multicuts in planar and bounded genus graphs with bounded number of terminals"
    - "all pairs minimum cuts in near linear time for surface embedded graphs"
    - "cops robbers and threatening skeletons padded decomposition for minor free graphs"
    - "fixed parameter tractability of multicut parameterized by the size of the cutset"
    - "polynomial time approximation schemes for subset connectivity problems in bounded genus graphs"
    - "approximation schemes for steiner forest on planar graphs and graphs of bounded treewidth"

Connected through hyperedge HE-2 (induced by source paper "global and fixed terminal cuts in digraphs"):
  with members:
    - "an improved integrality gap for the calinescu karloff rabani relaxation for multiway cut"
    - "improved hardness for cut interdiction and firefighter problems"
    - "simple and fast rounding algorithms for directed and node weighted multiway cut"
    - "directed multicut with linearly ordered terminals"
    - "multiway cut pairwise realizable distributions and descending thresholds"
    - "rounding algorithms for a geometric embedding of minimum multiway cut"

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)