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: "large scale correlation clustering optimization"
  Abstract: Clustering is a fundamental task in unsupervised learning. The focus of this paper is the Correlation Clustering functional which combines positive and negative affinities between the data points. The contribution of this paper is two fold: (i) Provide a theoretic analysis of the functional. (ii) New optimization algorithms which can cope with large scale problems (>100K variables) that are infeasible using existing methods. Our theoretic analysis provides a probabilistic generative interpretation for the functional, and justifies its intrinsic "model-selection" capability. Furthermore, we draw an analogy between optimizing this functional and the well known Potts energy minimization. This analogy allows us to suggest several new optimization algorithms, which exploit the intrinsic "model-selection" capability of the functional to automatically recover the underlying number of clusters. We compare our algorithms to existing methods on both synthetic and real data. In addition we suggest two new applications that are made possible by our algorithms: unsupervised face identification and interactive multi-object segmentation by rough boundary delineation.

Connected through hyperedge HE-1 (induced by source paper "digital technologies in airport ground operations"):
  with members:
    - "holistic measures for evaluating prediction models in smart grids"

Connected through hyperedge HE-2 (induced by source paper "correlation clustering in data streams"):
  with members:
    - "near optimal lp rounding algorithm for correlation clustering on complete and complete k partite graphs"
    - "access to data and number of iterations dual primal algorithms for maximum matching under resource constraints"
    - "a note on no need to choose how to get both a ptas and sublinear query complexity"
    - "linear programming in the semi streaming model with application to the maximum matching problem"
    - "ams without 4 wise independence on product domains"
    - "correlation clustering with a fixed number of clusters"

Connected through hyperedge HE-3 (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"
    - "lifting of multicuts"
    - "efficient decomposition of image and mesh graphs by lifted multicuts"
    - "fast planar correlation clustering for image segmentation"

Connected through hyperedge HE-4 (induced by source paper "unifying sparsest cut cluster deletion and modularity clustering objectives with correlation clustering"):
  with members:
    - "multiresolution consensus clustering in networks"

Connected through hyperedge HE-5 (induced by source paper "multicuts and perturb map for probabilistic graph clustering"):
  with members:
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "higher order segmentation via multicuts"
    - "on sampling from the gibbs distribution with random maximum a posteriori perturbations"
    - "opengm a c library for discrete graphical models"
    - "on the partition function and random maximum a posteriori perturbations"
    - "continuous multiclass labeling approaches and algorithms"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-6 (induced by source paper "moral lineage tracing"):
  with members:
    - "planar ultrametric rounding for image segmentation"
    - "lifting of multicuts"
    - "efficient decomposition of image and mesh graphs by lifted multicuts"
    - "higher order segmentation via multicuts"
    - "fast planar correlation clustering for image segmentation"

Connected through hyperedge HE-7 (induced by source paper "tracking with multi level features"):
  with members:
    - "temporal dynamic appearance modeling for online multi person tracking"
    - "motchallenge 2015 towards a benchmark for multi target tracking"
    - "learning to divide and conquer for online multi target tracking"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-8 (induced by source paper "efficient decomposition of image and mesh graphs by lifted multicuts"):
  with members:
    - "lifting of multicuts"
    - "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"

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.CV(Computer Vision and Pattern Recognition)