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: "predicting daily activities from egocentric images using deep learning"
  Abstract: We present a method to analyze images taken from a passive egocentric wearable camera along with the contextual information, such as time and day of week, to learn and predict everyday activities of an individual. We collected a dataset of 40,103 egocentric images over a 6 month period with 19 activity classes and demonstrate the benefit of state-of-the-art deep learning techniques for learning and predicting daily activities. Classification is conducted using a Convolutional Neural Network (CNN) with a classification method we introduce called a late fusion ensemble. This late fusion ensemble incorporates relevant contextual information and increases our classification accuracy. Our technique achieves an overall accuracy of 83.07% in predicting a person's activity across the 19 activity classes. We also demonstrate some promising results from two additional users by fine-tuning the classifier with one day of training data.

Connected through hyperedge HE-1 (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"
    - "sr clustering semantic regularized clustering for egocentric photo streams segmentation"
    - "going deeper into first person activity recognition"
    - "beyond short snippets deep networks for video classification"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-2 (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"
    - "sr clustering semantic regularized clustering for egocentric photo streams segmentation"
    - "xception deep learning with depthwise separable convolutions"
    - "compact cnn for indexing egocentric videos"

Connected through hyperedge HE-3 (induced by source paper "egocentric video description based on temporally linked sequences"):
  with members:
    - "batch based activity recognition from egocentric photo streams"
    - "hierarchical lstm with adjusted temporal attention for video captioning"
    - "learning to remember rare events"
    - "dense captioning events in videos"
    - "toward storytelling from visual lifelogging an overview"
    - "sr clustering semantic regularized clustering for egocentric photo streams segmentation"
    - "semantic summarization of egocentric photo stream events"
    - "attention based multimodal fusion for video description"

Connected through hyperedge HE-4 (induced by source paper "toward storytelling from visual lifelogging an overview"):
  with members:
    - "r clustering for egocentric video segmentation"
    - "semantic summarization of egocentric photo stream events"
    - "multi face tracking by extended bag of tracklets in egocentric photo streams"
    - "f formation detection individuating free standing conversational groups in images"
    - "a survey on recent advances of computer vision algorithms for egocentric video"
    - "leveraging context to support automated food recognition in restaurants"
    - "sequence to sequence video to text"
    - "egocentric field of view localization using first person point of view devices"

Connected through hyperedge HE-5 (induced by source paper "eyemotion classifying facial expressions in vr using eye tracking cameras"):
  with members:
    - "mask off synthesizing face images in the presence of head mounted displays"
    - "eye tracking for everyone"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"
    - "the nd iris 0405 iris image dataset"
    - "facevr real time facial reenactment and eye gaze control in virtual reality"
    - "peak piloted deep network for facial expression recognition"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-6 (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 important objects for egocentric video summarization"
    - "visual summary of egocentric photostreams by representative keyframes"
    - "seeing the big picture deep embedding with contextual evidences"

Connected through hyperedge HE-7 (induced by source paper "batch based activity recognition from egocentric photo streams"):
  with members:
    - "recognizing activities of daily living from egocentric images"
    - "r clustering for egocentric video segmentation"
    - "toward storytelling from visual lifelogging an overview"
    - "sr clustering semantic regularized clustering for egocentric photo streams segmentation"
    - "compact cnn for indexing egocentric videos"
    - "beyond short snippets deep networks for video classification"
    - "very deep convolutional networks for large scale image recognition"
    - "long term recurrent convolutional networks for visual recognition and description"

Connected through hyperedge HE-8 (induced by source paper "batch based monocular slam for egocentric videos"):
  with members:
    - "computing egomotion with local loop closures for egocentric videos"
    - "compact cnn for indexing egocentric videos"
    - "egosampling fast forward and stereo for egocentric videos"
    - "hierarchical structure and motion recovery from uncalibrated images"
    - "orb slam a versatile and accurate monocular slam system"
    - "robust camera location estimation by convex programming"

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)