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: "learning bag of features pooling for deep convolutional neural networks"
  Abstract: Convolutional Neural Networks (CNNs) are well established models capable of achieving state-of-the-art classification accuracy for various computer vision tasks. However, they are becoming increasingly larger, using millions of parameters, while they are restricted to handling images of fixed size. In this paper, a quantization-based approach, inspired from the well-known Bag-of-Features model, is proposed to overcome these limitations. The proposed approach, called Convolutional BoF (CBoF), uses RBF neurons to quantize the information extracted from the convolutional layers and it is able to natively classify images of various sizes as well as to significantly reduce the number of parameters in the network. In contrast to other global pooling operators and CNN compression techniques the proposed method utilizes a trainable pooling layer that it is end-to-end differentiable, allowing the network to be trained using regular back-propagation and to achieve greater distribution shift invariance than competitive methods. The ability of the proposed method to reduce the parameters of the network and increase the classification accuracy over other state-of-the-art techniques is demonstrated using three image datasets.

Connected through hyperedge HE-1 (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"
    - "mask r cnn"
    - "inductive bias of deep convolutional networks through pooling geometry"

Connected through hyperedge HE-2 (induced by source paper "bag of color features for color constancy"):
  with members:
    - "color constancy convolutional autoencoder"
    - "rehabilitating the color checker dataset for illuminant estimation"
    - "color constancy by gans an experimental survey"
    - "a data set for camera independent color constancy"
    - "attention is all you need"
    - "massive exploration of neural machine translation architectures"
    - "improving color constancy by discounting the variation of camera spectral sensitivity"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-3 (induced by source paper "temporal logistic neural bag of features for financial time series forecasting leveraging limit order book data"):
  with members:
    - "machine learning for forecasting mid price movement using limit order book data"
    - "benchmark dataset for mid price prediction of limit order book data"
    - "tensor representation in high frequency financial data for price change prediction"
    - "multi scale convolutional neural networks for time series classification"
    - "learning to diagnose with lstm recurrent neural networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "do place cells dream of conditional probabilities learning neural nystrom representations"):
  with members:
    - "on the convergence of adam and beyond"
    - "clustering is semidefinitely not that hard nonnegative sdp for manifold disentangling"
    - "enriching word vectors with subword information"
    - "deep fried convnets"
    - "convolutional kernel networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "learning deep representations with probabilistic knowledge transfer"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dsod learning deeply supervised object detectors from scratch"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources"
    - "yolo9000 better faster stronger"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "you only look once unified real time object detection"
    - "bayesian dark knowledge"

Connected through hyperedge HE-6 (induced by source paper "ts lstm and temporal inception exploiting spatiotemporal dynamics for activity recognition"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "attend and interact higher order object interactions for video understanding"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "attention clusters purely attention based local feature integration for video classification"
    - "hierarchical multi scale attention networks for action recognition"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "temporal segment networks towards good practices for deep action recognition"
    - "youtube 8m a large scale video classification benchmark"

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)