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: "learnable pooling regions for image classification"
  Abstract: Biologically inspired, from the early HMAX model to Spatial Pyramid Matching, pooling has played an important role in visual recognition pipelines. Spatial pooling, by grouping of local codes, equips these methods with a certain degree of robustness to translation and deformation yet preserving important spatial information. Despite the predominance of this approach in current recognition systems, we have seen little progress to fully adapt the pooling strategy to the task at hand. This paper proposes a model for learning task dependent pooling scheme -- including previously proposed hand-crafted pooling schemes as a particular instantiation. In our work, we investigate the role of different regularization terms showing that the smooth regularization term is crucial to achieve strong performance using the presented architecture. Finally, we propose an efficient and parallel method to train the model. Our experiments show improved performance over hand-crafted pooling schemes on the CIFAR-10 and CIFAR-100 datasets -- in particular improving the state-of-the-art to 56.29% on the latter.

Connected through hyperedge HE-1 (induced by source paper "frank wolfe network an interpretable deep structure for non sparse coding"):
  with members:
    - "learning simple thresholded features with sparse support recovery"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "ista net interpretable optimization inspired deep network for image compressive sensing"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "a unified optimization view on generalized matching pursuit and frank wolfe"
    - "maximal sparsity with deep networks"
    - "learning optimal nonlinearities for iterative thresholding algorithms"
    - "normalizing the normalizers comparing and extending network normalization schemes"

Connected through hyperedge HE-2 (induced by source paper "a closed form learned pooling for deep classification networks"):
  with members:
    - "making convolutional networks shift invariant again"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "detail preserving pooling in deep networks"
    - "why do deep convolutional networks generalize so poorly to small image transformations"
    - "polar transformer networks"
    - "learnable pooling with context gating for video classification"
    - "learning bag of features pooling for deep convolutional neural networks"
    - "mask r cnn"

Connected through hyperedge HE-3 (induced by source paper "learning bag of features pooling for deep convolutional neural networks"):
  with members:
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "quantized convolutional neural networks for mobile devices"
    - "deep residual learning for image recognition"
    - "learning to answer questions from image using convolutional neural network"
    - "compressing convolutional neural networks"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "multiple instance learning convolutional neural networks for object recognition"):
  with members:
    - "hcp a flexible cnn framework for multi label image classification"
    - "latent model ensemble with auto localization"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "going deeper with convolutions"

Connected through hyperedge HE-5 (induced by source paper "latent model ensemble with auto localization"):
  with members:
    - "generic object detection with dense neural patterns and regionlets"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "network in network"
    - "stochastic pooling for regularization of deep convolutional neural networks"
    - "multi column deep neural networks for image classification"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "high performance neural networks for visual object classification"

Connected through hyperedge HE-6 (induced by source paper "network in network"):
  with members:
    - "visualizing and understanding convolutional networks"
    - "stochastic pooling for regularization of deep convolutional neural networks"
    - "improving neural networks by preventing co adaptation of feature detectors"

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)