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: "attentional neural network feature selection using cognitive feedback"
  Abstract: Attentional Neural Network is a new framework that integrates top-down cognitive bias and bottom-up feature extraction in one coherent architecture. The top-down influence is especially effective when dealing with high noise or difficult segmentation problems. Our system is modular and extensible. It is also easy to train and cheap to run, and yet can accommodate complex behaviors. We obtain classification accuracy better than or competitive with state of art results on the MNIST variation dataset, and successfully disentangle overlaid digits with high success rates. We view such a general purpose framework as an essential foundation for a larger system emulating the cognitive abilities of the whole brain.

Connected through hyperedge HE-1 (induced by source paper "fsnet feature selection network on high dimensional biological data"):
  with members:
    - "concrete autoencoders for differentiable feature selection and reconstruction"
    - "deeppink reproducible feature selection in deep neural networks"
    - "diet networks thin parameters for fat genomic"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "compressing neural networks with the hashing trick"

Connected through hyperedge HE-2 (induced by source paper "self attention capsule networks for object classification"):
  with members:
    - "quantitative phase imaging and artificial intelligence a review"
    - "brain tumor type classification via capsule networks"
    - "hitnet a neural network with capsules embedded in a hit or miss layer extended with hybrid data augmentation and ghost capsules"
    - "attention u net learning where to look for the pancreas"
    - "dense and diverse capsule networks making the capsules learn better"
    - "non local neural networks"
    - "dynamic routing between capsules"
    - "densely connected convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "afs an attention based mechanism for supervised feature selection"):
  with members:
    - "context aware query image representation for particular object retrieval"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"

Connected through hyperedge HE-4 (induced by source paper "convolutional neural networks with alternately updated clique"):
  with members:
    - "squeeze and excitation networks"
    - "dual path networks"
    - "residual connections encourage iterative inference"
    - "residual attention network for image classification"
    - "dual attention networks for multimodal reasoning and matching"
    - "aggregated residual transformations for deep neural networks"
    - "deep networks with stochastic depth"
    - "densely connected convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "top down feedback for crowd counting convolutional neural network"):
  with members:
    - "switching convolutional neural network for crowd counting"
    - "optical flow estimation using a spatial pyramid network"
    - "beyond skip connections top down modulation for object detection"
    - "crowdnet a deep convolutional network for dense crowd counting"
    - "iterative instance segmentation"
    - "deep networks with internal selective attention through feedback connections"

Connected through hyperedge HE-6 (induced by source paper "learning qualitatively diverse and interpretable rules for classification"):
  with members:
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "why should i trust you explaining the predictions of any classifier"
    - "deep learning of representations looking forward"

Connected through hyperedge HE-7 (induced by source paper "dynamic computational time for visual attention"):
  with members:
    - "learning to reason with adaptive computation"
    - "low rank bilinear pooling for fine grained classification"
    - "stacked hourglass networks for human pose estimation"
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"
    - "adaptive computation time for recurrent neural networks"
    - "localizing by describing attribute guided attention localization for fine grained recognition"
    - "spatially adaptive computation time for residual networks"
    - "mining discriminative triplets of patches for fine grained classification"

Connected through hyperedge HE-8 (induced by source paper "learning with rethinking recurrently improving convolutional neural networks through feedback"):
  with members:
    - "approaching the computational color constancy as a classification problem through deep learning"
    - "towards better exploiting convolutional neural networks for remote sensing scene classification"
    - "seven ways to improve example based single image super resolution"
    - "dropsample a new training method to enhance deep convolutional neural networks for large scale unconstrained handwritten chinese character recognition"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "deeply recursive convolutional network for image super resolution"
    - "faster r cnn towards real time object detection with region proposal networks"

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)