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: "the treasure beneath convolutional layers cross convolutional layer pooling for image classification"
  Abstract: A number of recent studies have shown that a Deep Convolutional Neural Network (DCNN) pretrained on a large dataset can be adopted as a universal image description which leads to astounding performance in many visual classification tasks. Most of these studies, if not all, adopt activations of the fully-connected layer of a DCNN as the image or region representation and it is believed that convolutional layer activations are less discriminative. #R##N#This paper, however, advocates that if used appropriately convolutional layer activations can be turned into a powerful image representation which enjoys many advantages over fully-connected layer activations. This is achieved by adopting a new technique proposed in this paper called cross-convolutional-layer pooling. More specifically, it extracts subarrays of feature maps of one convolutional layer as local features and pools the extracted features with the guidance of feature maps of the successive convolutional layer. Compared with exising methods that apply DCNNs in the local feature setting, the proposed method is significantly faster since it requires much fewer times of DCNN forward computation. Moreover, it avoids the domain mismatch issue which is usually encountered when applying fully connected layer activations to describe local regions. By applying our method to four popular visual classification tasks, it is demonstrated that the proposed method can achieve comparable or in some cases significantly better performance than existing fully-connected layer based image representations while incurring much lower computational cost.

Connected through hyperedge HE-1 (induced by source paper "multifaceted analysis of fine tuning in deep model for visual recognition"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "gabor convolutional networks"
    - "taskonomy disentangling task transfer learning"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "learning transferable features with deep adaptation networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "look no deeper recognizing places from opposing viewpoints under varying scene appearance using single view depth estimation"):
  with members:
    - "don t look back robustifying place categorization for viewpoint and condition invariant place recognition"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
    - "a deeper insight into the undemon unsupervised deep network for depth and ego motion estimation"
    - "eng end to end neural geometry for robust depth and pose estimation using cnns"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "x view graph based semantic multi view localization"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"

Connected through hyperedge HE-3 (induced by source paper "classification driven single image dehazing"):
  with members:
    - "does haze removal help cnn based image classification"
    - "gated fusion network for single image dehazing"
    - "densely connected pyramid dehazing network"
    - "squeeze and excitation networks"
    - "image de raining using a conditional generative adversarial network"
    - "residual attention network for image classification"
    - "perceptual losses for real time style transfer and super resolution"
    - "dehazenet an end to end system for single image haze removal"

Connected through hyperedge HE-4 (induced by source paper "enhance the motion cues for face anti spoofing using cnn lstm architecture"):
  with members:
    - "face anti spoofing based on color texture analysis"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "recurrent models of visual attention"
    - "neural machine translation by jointly learning to align and translate"
    - "fully convolutional networks for semantic segmentation"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "parameter optimization and learning in a spiking neural network for uav obstacle avoidance targeting neuromorphic processors"):
  with members:
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "rank pooling for action recognition"
    - "neuromorphic electronic circuits for building autonomous cognitive systems"
    - "evolution of plastic learning in spiking networks via memristive connections"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "scale coding bag of deep features for human attribute and action recognition"):
  with members:
    - "scene recognition with cnns objects scales and dataset bias"
    - "bilinear cnn models for fine grained visual recognition"
    - "fuzzy human motion analysis"
    - "multi scale recognition with dag cnns"
    - "an expressive deep model for human action parsing from a single image"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "r cnns for pose estimation and action detection"

Connected through hyperedge HE-7 (induced by source paper "does haze removal help cnn based image classification"):
  with members:
    - "an all in one network for dehazing and beyond"
    - "residual attention network for image classification"
    - "dehazenet an end to end system for single image haze removal"
    - "joint visual denoising and classification using deep learning"
    - "cnn rnn a unified framework for multi label image classification"
    - "studying very low resolution recognition using deep networks"
    - "deep residual learning for image recognition"
    - "understanding deep image representations by inverting them"

Connected through hyperedge HE-8 (induced by source paper "mining object parts from cnns via active question answering"):
  with members:
    - "growing interpretable part graphs on convnets via multi shot learning"
    - "webly supervised learning of convolutional networks"
    - "inverting visual representations with convolutional networks"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "understanding deep features with computer generated imagery"
    - "fast r cnn"
    - "part localization using multi proposal consensus for fine grained categorization"
    - "unsupervised object discovery and localization in the wild part based matching with bottom up region proposals"

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)