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: "spotlight the negatives a generalized discriminative latent model"
  Abstract: Discriminative latent variable models (LVM) are frequently applied to various visual recognition tasks. In these systems the latent (hidden) variables provide a formalism for modeling structured variation of visual features. Conventionally, latent variables are de- fined on the variation of the foreground (positive) class. In this work we augment LVMs to include negative latent variables corresponding to the background class. We formalize the scoring function of such a generalized LVM (GLVM). Then we discuss a framework for learning a model based on the GLVM scoring function. We theoretically showcase how some of the current visual recognition methods can benefit from this generalization. Finally, we experiment on a generalized form of Deformable Part Models with negative latent variables and show significant improvements on two different detection tasks.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised object localization using min max entropy an interpretable framework"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "min entropy latent model for weakly supervised object detection"
    - "constrained cnn losses for weakly supervised segmentation"
    - "visual interpretability for deep learning a survey"
    - "dropblock a regularization method for convolutional networks"
    - "attention based deep multiple instance learning"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"

Connected through hyperedge HE-2 (induced by source paper "min max entropy for weakly supervised pointwise localization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "min entropy latent model for weakly supervised object detection"
    - "constrained cnn losses for weakly supervised segmentation"
    - "visual interpretability for deep learning a survey"
    - "dropblock a regularization method for convolutional networks"
    - "attention based deep multiple instance learning"
    - "adversarial complementary learning for weakly supervised object localization"
    - "tell me where to look guided attention inference network"

Connected through hyperedge HE-3 (induced by source paper "no spare parts sharing part detectors for image categorization"):
  with members:
    - "object localization in imagenet by looking out of the window"
    - "mid level deep pattern mining"
    - "spatial transformer networks"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "exemplar based deep discriminative and shareable feature learning for scene image classification"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "object detectors emerge in deep scene cnns"

Connected through hyperedge HE-4 (induced by source paper "generalized majorization minimization"):
  with members:
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "on learning to localize objects with minimal supervision"
    - "modeling latent variable uncertainty for loss based learning"
    - "on the convergence of bound optimization algorithms"

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)