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: "on the behavior of convolutional nets for feature extraction"
  Abstract: Deep neural networks are representation learning techniques. During training, a deep net is capable of generating a descriptive language of unprecedented size and detail in machine learning. Extracting the descriptive language coded within a trained CNN model (in the case of image data), and reusing it for other purposes is a field of interest, as it provides access to the visual descriptors previously learnt by the CNN after processing millions of images, without requiring an expensive training phase. Contributions to this field (commonly known as feature representation transfer or transfer learning) have been purely empirical so far, extracting all CNN features from a single layer close to the output and testing their performance by feeding them to a classifier. This approach has provided consistent results, although its relevance is limited to classification tasks. In a completely different approach, in this paper we statistically measure the discriminative power of every single feature found within a deep CNN, when used for characterizing every class of 11 datasets. We seek to provide new insights into the behavior of CNN features, particularly the ones from convolutional layers, as this can be relevant for their application to knowledge representation and reasoning. Our results confirm that low and middle level features may behave differently to high level features, but only under certain conditions. We find that all CNN features can be used for knowledge representation purposes both by their presence or by their absence, doubling the information a single CNN feature may provide. We also study how much noise these features may include, and propose a thresholding approach to discard most of it. All these insights have a direct application to the generation of CNN embedding spaces.

Connected through hyperedge HE-1 (induced by source paper "classification of crop tolerance to heat and drought a deep convolutional neural networks approach"):
  with members:
    - "crop yield prediction using deep neural networks"
    - "deep learning in agriculture a survey"
    - "tensorflow a system for large scale machine learning"
    - "a critical review of recurrent neural networks for sequence learning"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "factors of transferability for a generic convnet representation"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-2 (induced by source paper "feature discriminativity estimation in cnns for transfer learning"):
  with members:
    - "places an image database for deep scene understanding"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "factors of transferability for a generic convnet representation"
    - "imagenet large scale visual recognition challenge"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "describing textures in the wild"

Connected through hyperedge HE-3 (induced by source paper "predicting visual memory schemas with variational autoencoders"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "defining image memorability using the visual memory schema"
    - "amnet memorability estimation with attention"
    - "do semantic parts emerge in convolutional neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "object detectors emerge in deep scene cnns"

Connected through hyperedge HE-4 (induced by source paper "reusing convolutional activations from frame to frame to speed up training and inference"):
  with members:
    - "model based reinforcement learning for atari"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-5 (induced by source paper "full network embedding in a multimodal embedding pipeline"):
  with members:
    - "an out of the box full network embedding for convolutional neural networks"
    - "word2visualvec image and video to sentence matching by visual feature prediction"
    - "image captioning with deep bidirectional lstms"
    - "multimodal convolutional neural networks for matching image and sentence"
    - "deep convolutional features for image based retrieval and scene categorization"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "unifying visual semantic embeddings with multimodal neural language models"

Connected through hyperedge HE-6 (induced by source paper "building graph representations of deep vector embeddings"):
  with members:
    - "an out of the box full network embedding for convolutional neural networks"
    - "fluid communities a competitive scalable and diverse community detection algorithm"
    - "borrowing treasures from the wealthy deep transfer learning through selective joint fine tuning"
    - "deepfood deep learning based food image recognition for computer aided dietary assessment"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "deep convolutional features for image based retrieval and scene categorization"
    - "deepwalk online learning of social representations"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "an out of the box full network embedding for convolutional neural networks"):
  with members:
    - "borrowing treasures from the wealthy deep transfer learning through selective joint fine tuning"
    - "places an image database for deep scene understanding"
    - "learning transferable features with deep adaptation networks"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep convolutional features for image based retrieval and scene categorization"
    - "very deep convolutional networks for large scale image recognition"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"

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.NE(Neural and Evolutionary Computing)