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: "seeing the big picture deep embedding with contextual evidences"
  Abstract: In the Bag-of-Words (BoW) model based image retrieval task, the precision of visual matching plays a critical role in improving retrieval performance. Conventionally, local cues of a keypoint are employed. However, such strategy does not consider the contextual evidences of a keypoint, a problem which would lead to the prevalence of false matches. To address this problem, this paper defines "true match" as a pair of keypoints which are similar on three levels, i.e., local, regional, and global. Then, a principled probabilistic framework is established, which is capable of implicitly integrating discriminative cues from all these feature levels. #R##N#Specifically, the Convolutional Neural Network (CNN) is employed to extract features from regional and global patches, leading to the so-called "Deep Embedding" framework. CNN has been shown to produce excellent performance on a dozen computer vision tasks such as image classification and detection, but few works have been done on BoW based image retrieval. In this paper, firstly we show that proper pre-processing techniques are necessary for effective usage of CNN feature. Then, in the attempt to fit it into our model, a novel indexing structure called "Deep Indexing" is introduced, which dramatically reduces memory usage. #R##N#Extensive experiments on three benchmark datasets demonstrate that, the proposed Deep Embedding method greatly promotes the retrieval accuracy when CNN feature is integrated. We show that our method is efficient in terms of both memory and time cost, and compares favorably with the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "hierarchical approach to classify food scenes in egocentric photo streams"):
  with members:
    - "batch based activity recognition from egocentric photo streams revisited"
    - "macnet multi scale atrous convolution networks for food places classification in egocentric photo streams"
    - "r clustering for egocentric video segmentation"
    - "sr clustering semantic regularized clustering for egocentric photo streams segmentation"
    - "places an image database for deep scene understanding"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "scene recognition with cnns objects scales and dataset bias"
    - "object scene convolutional neural networks for event recognition in images"

Connected through hyperedge HE-2 (induced by source paper "towards unsupervised familiar scene recognition in egocentric videos"):
  with members:
    - "r clustering for egocentric video segmentation"

Connected through hyperedge HE-3 (induced by source paper "sr clustering semantic regularized clustering for egocentric photo streams segmentation"):
  with members:
    - "r clustering for egocentric video segmentation"
    - "toward storytelling from visual lifelogging an overview"
    - "multi face tracking by extended bag of tracklets in egocentric photo streams"
    - "with whom do i interact detecting social interactions in egocentric photo streams"
    - "compact cnn for indexing egocentric videos"
    - "predicting daily activities from egocentric images using deep learning"
    - "predicting important objects for egocentric video summarization"
    - "visual summary of egocentric photostreams by representative keyframes"

Connected through hyperedge HE-4 (induced by source paper "sentiment recognition in egocentric photostreams"):
  with members:
    - "r clustering for egocentric video segmentation"
    - "sr clustering semantic regularized clustering for egocentric photo streams segmentation"
    - "building a large scale dataset for image emotion recognition the fine print and the benchmark"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "diving deep into sentiment understanding fine tuned cnns for visual sentiment prediction"
    - "deepsentibank visual sentiment concept classification with deep convolutional neural networks"
    - "learning face representation from scratch"

Connected through hyperedge HE-5 (induced by source paper "r clustering for egocentric video segmentation"):
  with members:
    - "caffe convolutional architecture for fast feature embedding"
    - "multi digit number recognition from street view imagery using deep convolutional neural networks"
    - "methods of hierarchical clustering"

Connected through hyperedge HE-6 (induced by source paper "image retrieval with hierarchical matching pursuit"):
  with members:
    - "visual reranking with improved image graph"
    - "packing and padding coupled multi index for accurate image retrieval"
    - "bayes merging of multiple vocabularies for scalable image retrieval"

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)