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: "fisher kernel for deep neural activations"
  Abstract: Compared to image representation based on low-level local descriptors, deep neural activations of Convolutional Neural Networks (CNNs) are richer in mid-level representation, but poorer in geometric invariance properties. In this paper, we present a straightforward framework for better image representation by combining the two approaches. To take advantages of both representations, we propose an efficient method to extract a fair amount of multi-scale dense local activations from a pre-trained CNN. We then aggregate the activations by Fisher kernel framework, which has been modified with a simple scale-wise normalization essential to make it suitable for CNN activations. Replacing the direct use of a single activation vector with our representation demonstrates significant performance improvements: +17.76 (Acc.) on MIT Indoor 67 and +7.18 (mAP) on PASCAL VOC 2007. The results suggest that our proposal can be used as a primary image representation for better performances in visual recognition tasks.

Connected through hyperedge HE-1 (induced by source paper "semantic aware scene recognition"):
  with members:
    - "auto reid searching for a part aware convnet for person re identification"
    - "rethinking imagenet pre training"
    - "deep frank wolfe for neural network optimization"
    - "taskonomy disentangling task transfer learning"
    - "unified perceptual parsing for scene understanding"
    - "improving the performance of unimodal dynamic hand gesture recognition with multimodal training"
    - "in pixels we trust from pixel labeling to object localization and scene categorization"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-2 (induced by source paper "hybrid cnn and dictionary based models for scene recognition and domain adaptation"):
  with members:
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "learning transferable features with deep adaptation networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "places205 vggnet models for scene recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "how transferable are features in deep neural networks"
    - "deepid net deformable deep convolutional neural networks for object detection"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "backpropagation training for fisher vectors within neural networks"):
  with members:
    - "fisher vectors derived from hybrid gaussian laplacian mixture models for image annotation"
    - "deep convolutional filter banks for texture recognition and segmentation"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised fine grained categorization with part based image representation"):
  with members:
    - "spatial transformer networks"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "fast r cnn"
    - "r cnn minus r"
    - "a comparison of dense region detectors for image search and fine grained classification"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "fine grained recognition datasets for biodiversity analysis"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "compositional model based fisher vector coding for image classification"):
  with members:
    - "approximate fisher kernels of non iid image models for image categorization"
    - "bilinear cnn models for fine grained visual recognition"
    - "mid level deep pattern mining"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "encoding high dimensional local features by sparse coding based fisher vectors"
    - "part based r cnns for fine grained category detection"

Connected through hyperedge HE-6 (induced by source paper "dense image representation with spatial pyramid vlad coding of cnn for locally robust captioning"):
  with members:
    - "densecap fully convolutional localization networks for dense captioning"
    - "visualizing and understanding recurrent networks"
    - "very deep convolutional networks for large scale image recognition"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "cider consensus based image description evaluation"
    - "caffe convolutional architecture for fast feature embedding"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "from captions to visual concepts and back"

Connected through hyperedge HE-7 (induced by source paper "cross convolutional layer pooling for image recognition"):
  with members:
    - "texture synthesis using shallow convolutional networks with random filters"
    - "bilinear cnn models for fine grained visual recognition"
    - "mid level deep pattern mining"
    - "mining mid level visual patterns with deep cnn activations"
    - "fast r cnn"
    - "texture synthesis using convolutional neural networks"
    - "aggregating deep convolutional features for image retrieval"
    - "analyzing the performance of multilayer neural networks for object recognition"

Connected through hyperedge HE-8 (induced by source paper "attentionnet aggregating weak directions for accurate object detection"):
  with members:
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "deep poselets for human detection"
    - "analyzing the performance of multilayer neural networks for object recognition"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "deepid net deformable deep convolutional neural networks for object detection"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"

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)