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: "sr clustering semantic regularized clustering for egocentric photo streams segmentation"
  Abstract: While wearable cameras are becoming increasingly popular, locating relevant information in large unstructured collections of egocentric images is still a tedious and time consuming process. This paper addresses the problem of organizing egocentric photo streams acquired by a wearable camera into semantically meaningful segments, hence making an important step towards the goal of automatically annotating these photos for browsing and retrieval. In the proposed method, first, contextual and semantic information is extracted for each image by employing a Convolutional Neural Networks approach. Later, a vocabulary of concepts is defined in a semantic space by relying on linguistic information. Finally, by exploiting the temporal coherence of concepts in photo streams, images which share contextual and semantic attributes are grouped together. The resulting temporal segmentation is particularly suited for further analysis, ranging from event recognition to semantic indexing and summarization. Experimental results over egocentric set of nearly 31,000 images, show the prominence of the proposed approach over state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "learning event representations in image sequences by dynamic graph embedding"):
  with members:
    - "predicting visual context for unsupervised event segmentation in continuous photo streams"
    - "egocentric video description based on temporally linked sequences"
    - "r clustering for egocentric video segmentation"
    - "harp hierarchical representation learning for networks"
    - "representation learning on graphs methods and applications"
    - "toward storytelling from visual lifelogging an overview"
    - "geometric deep learning going beyond euclidean data"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-2 (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"
    - "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"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"

Connected through hyperedge HE-3 (induced by source paper "social relation recognition in egocentric photostreams"):
  with members:
    - "person recognition in personal photo collections"
    - "batch based activity recognition from egocentric photo streams revisited"
    - "a domain based approach to social relation recognition"
    - "toward storytelling from visual lifelogging an overview"
    - "dockerface an easy to install and use faster r cnn face detector in a docker container"
    - "an overview of multi task learning in deep neural networks"
    - "with whom do i interact detecting social interactions in egocentric photo streams"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "towards emotion retrieval in egocentric photostream"):
  with members:
    - "sentiment recognition in egocentric photostreams"
    - "going deeper into first person activity recognition"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "batch based activity recognition from egocentric photo streams revisited"):
  with members:
    - "recognizing activities of daily living from egocentric images"
    - "batch based activity recognition from egocentric photo streams"
    - "toward storytelling from visual lifelogging an overview"
    - "going deeper into first person activity recognition"
    - "beyond short snippets deep networks for video classification"
    - "deep residual learning for image recognition"
    - "predicting daily activities from egocentric images using deep learning"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-6 (induced by source paper "predicting visual context for unsupervised event segmentation in continuous photo streams"):
  with members:
    - "phd gifs personalized highlight detection for automatic gif creation"
    - "egocentric video description based on temporally linked sequences"
    - "toward storytelling from visual lifelogging an overview"
    - "hierarchically attentive rnn for album summarization and storytelling"
    - "compact cnn for indexing egocentric videos"
    - "discovering picturesque highlights from egocentric vacation videos"
    - "unsupervised learning of video representations using lstms"
    - "predicting important objects for egocentric video summarization"

Connected through hyperedge HE-7 (induced by source paper "on the role of event boundaries in egocentric activity recognition from photostreams"):
  with members:
    - "batch based activity recognition from egocentric photo streams revisited"
    - "who let the dogs out modeling dog behavior from visual data"
    - "recognizing activities of daily living from egocentric images"
    - "batch based activity recognition from egocentric photo streams"
    - "xception deep learning with depthwise separable convolutions"
    - "compact cnn for indexing egocentric videos"
    - "predicting daily activities from egocentric images using deep learning"

Connected through hyperedge HE-8 (induced by source paper "macnet multi scale atrous convolution networks for food places classification in egocentric photo streams"):
  with members:
    - "a robust adaptive stochastic gradient method for deep learning"
    - "toward storytelling from visual lifelogging an overview"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "deep residual learning for 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.AI(Artificial Intelligence)