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: "an out of the box full network embedding for convolutional neural networks"
  Abstract: Transfer learning for feature extraction can be used to exploit deep representations in contexts where there is very few training data, where there are limited computational resources, or when tuning the hyper-parameters needed for training is not an option. While previous contributions to feature extraction propose embeddings based on a single layer of the network, in this paper we propose a full-network embedding which successfully integrates convolutional and fully connected features, coming from all layers of a deep convolutional neural network. To do so, the embedding normalizes features in the context of the problem, and discretizes their values to reduce noise and regularize the embedding space. Significantly, this also reduces the computational cost of processing the resultant representations. The proposed method is shown to outperform single layer embeddings on several image classification tasks, while also being more robust to the choice of the pre-trained model used for obtaining the initial features. The performance gap in classification accuracy between thoroughly tuned solutions and the full-network embedding is also reduced, which makes of the proposed approach a competitive solution for a large set of applications.

Connected through hyperedge HE-1 (induced by source paper "full network embedding in a multimodal embedding pipeline"):
  with members:
    - "on the behavior of convolutional nets for feature extraction"
    - "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-2 (induced by source paper "building graph representations of deep vector embeddings"):
  with members:
    - "fluid communities a competitive scalable and diverse community detection algorithm"
    - "on the behavior of convolutional nets for feature extraction"
    - "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-3 (induced by source paper "on the behavior of convolutional nets for feature extraction"):
  with members:
    - "full network embedding in a multimodal embedding pipeline"
    - "resfeats residual network based features for image classification"
    - "places an image database for deep scene understanding"
    - "understanding neural networks through deep visualization"
    - "learning transferable features with deep adaptation networks"
    - "deep residual learning for image recognition"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image 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.LG(Machine Learning)