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: "lifting of multicuts"
  Abstract: For every simple, undirected graph $G = (V, E)$, a one-to-one relation exists between the decompositions and the multicuts of $G$. A decomposition of $G$ is a partition $\Pi$ of $V$ such that, for every $U \in \Pi$, the subgraph of $G$ induced by $U$ is connected. A multicut of $G$ is an $M \subseteq E$ such that, for every (chordless) cycle $C \subseteq E$ of $G$, $|M \cap C| \neq 1$. The multicut related to a decomposition is the set of edges that straddle distinct components. The characteristic function $x \in \{0, 1\}^E$ of a multicut $M = x^{-1}(1)$ of $G$ makes explicit, for every pair $\{v,w\} \in E$ of neighboring nodes, whether $v$ and $w$ are in distinct components. In order to make explicit also for non-neighboring nodes, specifically, for all $\{v,w\} \in E'$ with $E \subseteq E' \subseteq {V \choose 2}$, whether $v$ and $w$ are in distinct components, we define a lifting of the multicuts of $G$ to multicuts of $G' = (V, E')$. We show that, if $G$ is connected, the convex hull of the characteristic functions of those multicuts of $G'$ that are lifted from $G$ is an $|E'|$-dimensional polytope in $\mathbb{R}^{E'}$. We establish properties of trivial facets of this polytope.

Connected through hyperedge HE-1 (induced by source paper "the mutex watershed and its objective efficient parameter free image partitioning"):
  with members:
    - "learned watershed end to end learning of seeded segmentation"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "learning steerable filters for rotation equivariant cnns"
    - "anisotropic em segmentation by 3d affinity learning and agglomeration"
    - "multi stage multi recursive input fully convolutional networks for neuronal boundary detection"
    - "fusionnet a deep fully residual convolutional neural network for image segmentation in connectomics"
    - "a multi cut formulation for joint segmentation and tracking of multiple objects"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-2 (induced by source paper "efficient algorithms for moral lineage tracing"):
  with members:
    - "the minimum cost connected subgraph problem in medical image analysis"
    - "arttrack articulated multi person tracking in the wild"
    - "moral lineage tracing"
    - "efficient decomposition of image and mesh graphs by lifted multicuts"
    - "fast planar correlation clustering for image segmentation"
    - "large scale correlation clustering optimization"

Connected through hyperedge HE-3 (induced by source paper "joint graph decomposition and node labeling by local search"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "the minimum cost connected subgraph problem in medical image analysis"
    - "mot16 a benchmark for multi object tracking"
    - "end to end instance segmentation and counting with recurrent attention"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "end to end instance segmentation with recurrent attention"
    - "instance aware semantic segmentation via multi task network cascades"
    - "proposal free network for instance level object segmentation"

Connected through hyperedge HE-4 (induced by source paper "moral lineage tracing"):
  with members:
    - "planar ultrametric rounding for image segmentation"
    - "efficient decomposition of image and mesh graphs by lifted multicuts"
    - "higher order segmentation via multicuts"
    - "fast planar correlation clustering for image segmentation"
    - "large scale correlation clustering optimization"

Connected through hyperedge HE-5 (induced by source paper "efficient decomposition of image and mesh graphs by lifted multicuts"):
  with members:
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "deepedge a multi scale bifurcated deep network for top down contour detection"
    - "oriented edge forests for boundary detection"
    - "fast planar correlation clustering for image segmentation"
    - "opengm a c library for discrete graphical models"
    - "large scale correlation clustering optimization"

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)