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: "potts model parametric maxflow and k submodular functions"
  Abstract: The problem of minimizing the Potts energy function frequently occurs in computer vision applications. One way to tackle this NP-hard problem was proposed by Kovtun [19,20]. It identifies a part of an optimal solution by running $k$ maxflow computations, where $k$ is the number of labels. The number of "labeled" pixels can be significant in some applications, e.g. 50-93% in our tests for stereo. We show how to reduce the runtime to $O(\log k)$ maxflow computations (or one {\em parametric maxflow} computation). Furthermore, the output of our algorithm allows to speed-up the subsequent alpha expansion for the unlabeled part, or can be used as it is for time-critical applications. #R##N#To derive our technique, we generalize the algorithm of Felzenszwalb et al. [7] for {\em Tree Metrics}. We also show a connection to {\em $k$-submodular functions} from combinatorial optimization, and discuss {\em $k$-submodular relaxations} for general energy functions.

Connected through hyperedge HE-1 (induced by source paper "efficient optimization for hierarchically structured interacting segments hints"):
  with members:
    - "volumetric bias in segmentation and reconstruction secrets and solutions"
    - "map estimation of semi metric mrfs via hierarchical graph cuts"

Connected through hyperedge HE-2 (induced by source paper "on maximizing a monotone k submodular function subject to a matroid constraint"):
  with members:
    - "maximizing k submodular functions and beyond"
    - "improved approximation algorithms for k submodular function maximization"
    - "towards minimizing k submodular functions"
    - "a tight combinatorial algorithm for submodular maximization subject to a matroid constraint"
    - "adaptive submodular optimization under matroid constraints"

Connected through hyperedge HE-3 (induced by source paper "derandomization for k submodular maximization"):
  with members:
    - "maximizing k submodular functions and beyond"
    - "improved approximation algorithms for k submodular function maximization"
    - "deterministic algorithms for submodular maximization problems"
    - "towards minimizing k submodular functions"

Connected through hyperedge HE-4 (induced by source paper "higher order maximum persistency and comparison theorems"):
  with members:
    - "a new look at reweighted message passing"
    - "maximum persistency via iterative relaxed inference with graphical models"
    - "maximum persistency in energy minimization"
    - "partial optimality by pruning for map inference with general graphical models"
    - "generalized sequential tree reweighted message passing"
    - "the power of linear programming for valued csps"
    - "efficiently searching for frustrated cycles in map inference"
    - "the power of linear programming for valued csps a constructive characterization"

Connected through hyperedge HE-5 (induced by source paper "maximum persistency via iterative relaxed inference with graphical models"):
  with members:
    - "higher order maximum persistency and comparison theorems"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "maximum persistency in energy minimization"
    - "partial optimality by pruning for map inference with general graphical models"
    - "efficient mrf energy minimization via adaptive diminishing smoothing"
    - "getting feasible variable estimates from infeasible ones mrf local polytope study"
    - "the power of linear programming for valued csps a constructive characterization"
    - "generalized roof duality and bisubmodular functions"

Connected through hyperedge HE-6 (induced by source paper "maximum persistency in energy minimization"):
  with members:
    - "partial optimality by pruning for map inference with general graphical models"
    - "generalized roof duality and bisubmodular functions"

Connected through hyperedge HE-7 (induced by source paper "a multi plane block coordinate frank wolfe algorithm for training structural svms with a costly max oracle"):
  with members:
    - "block coordinate frank wolfe optimization for structural svms"

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)