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: "volumetric bias in segmentation and reconstruction secrets and solutions"
  Abstract: Many standard optimization methods for segmentation and reconstruction compute ML model estimates for appearance or geometry of segments, e.g. Zhu-Yuille 1996, Torr 1998, Chan-Vese 2001, GrabCut 2004, Delong et al. 2012. We observe that the standard likelihood term in these formulations corresponds to a generalized probabilistic K-means energy. In learning it is well known that this energy has a strong bias to clusters of equal size, which can be expressed as a penalty for KL divergence from a uniform distribution of cardinalities. However, this volumetric bias has been mostly ignored in computer vision. We demonstrate significant artifacts in standard segmentation and reconstruction methods due to this bias. Moreover, we propose binary and multi-label optimization techniques that either (a) remove this bias or (b) replace it by a KL divergence term for any given target volume distribution. Our general ideas apply to many continuous or discrete energy formulations in segmentation, stereo, and other reconstruction problems.

Connected through hyperedge HE-1 (induced by source paper "constrained cnn losses for weakly supervised segmentation"):
  with members:
    - "constrained deep learning using conditional gradient and applications in computer vision"
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "imposing hard constraints on deep networks promises and limitations"
    - "learning random walk label propagation for weakly supervised semantic segmentation"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "deep clustering on the link between discriminative models and k means"):
  with members:
    - "clustering with deep learning taxonomy and new methods"
    - "deep clustering for unsupervised learning of visual features"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"
    - "training neural networks without gradients a scalable admm approach"
    - "joint unsupervised learning of deep representations and image clusters"
    - "unsupervised deep embedding for clustering analysis"
    - "adam a method for stochastic optimization"
    - "a submodular supermodular procedure with applications to discriminative structure learning"

Connected through hyperedge HE-3 (induced by source paper "efficient optimization for hierarchically structured interacting segments hints"):
  with members:
    - "potts model parametric maxflow and k submodular functions"
    - "map estimation of semi metric mrfs via hierarchical graph cuts"

Connected through hyperedge HE-4 (induced by source paper "kernel cuts mrf meets kernel spectral clustering"):
  with members:
    - "secrets of grabcut and kernel k means"
    - "kernel methods on riemannian manifolds with gaussian rbf kernels"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "scalable kernel clustering approximate kernel k means"
    - "an information theoretic analysis of hard and soft assignment methods for clustering"
    - "the k modes algorithm for clustering"
    - "efficient inference in fully connected crfs with gaussian edge potentials"
    - "a submodular supermodular procedure with applications to discriminative structure learning"

Connected through hyperedge HE-5 (induced by source paper "secrets of grabcut and kernel k means"):
  with members:
    - "kernel methods on riemannian manifolds with gaussian rbf kernels"
    - "scalable kernel clustering approximate kernel k means"
    - "an information theoretic analysis of hard and soft assignment methods for clustering"
    - "a tutorial on spectral clustering"

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)