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: "a message passing algorithm for the minimum cost multicut problem"
  Abstract: We propose a dual decomposition and linear program relaxation of the NP -hard minimum cost multicut problem. Unlike other polyhedral relaxations of the multicut polytope, it is amenable to efficient optimization by message passing. Like other polyhedral elaxations, it can be tightened efficiently by cutting planes. We define an algorithm that alternates between message passing and efficient separation of cycle- and odd-wheel inequalities. This algorithm is more efficient than state-of-the-art algorithms based on linear programming, including algorithms written in the framework of leading commercial software, as we show in experiments with large instances of the problem from applications in computer vision, biomedical image analysis and data mining.

Connected through hyperedge HE-1 (induced by source paper "massively parallel benders decomposition for correlation clustering"):
  with members:
    - "accelerating message passing for map with benders decomposition"
    - "exploiting skeletal structure in computer vision annotation with benders decomposition"
    - "deepercut a deeper stronger and faster multi person pose estimation model"
    - "deepcut joint subset partition and labeling for multi person pose estimation"
    - "efficient decomposition of image and mesh graphs by lifted multicuts"
    - "next generation multicuts for semi planar graphs"
    - "parallel correlation clustering on big graphs"
    - "fast planar correlation clustering for image segmentation"

Connected through hyperedge HE-2 (induced by source paper "end to end learning for graph decomposition"):
  with members:
    - "end to end learning of multi sensor 3d tracking by detection"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "thin slicing network a deep structured model for pose estimation in videos"
    - "lstm pose machines"
    - "optnet differentiable optimization as a layer in neural networks"
    - "towards accurate multi person pose estimation in the wild"
    - "deep network flow for multi object tracking"

Connected through hyperedge HE-3 (induced by source paper "discrete continuous admm for transductive inference in higher order mrfs"):
  with members:
    - "analysis and optimization of loss functions for multiclass top k and multilabel classification"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "a new look at reweighted message passing"
    - "secrets of grabcut and kernel k means"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "a convex relaxation for weakly supervised classifiers"

Connected through hyperedge HE-4 (induced by source paper "a dual ascent framework for lagrangean decomposition of combinatorial problems"):
  with members:
    - "a new look at reweighted message passing"
    - "efficient decomposition of image and mesh graphs by lifted multicuts"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "fast planar correlation clustering for image segmentation"
    - "convergent message passing algorithms a unifying view"
    - "lagrangian relaxation for map estimation in graphical models"

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)