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: "recognizing activities of daily living from egocentric images"
  Abstract: Recognizing Activities of Daily Living (ADLs) has a large number of health applications, such as characterize lifestyle for habit improvement, nursing and rehabilitation services. Wearable cameras can daily gather large amounts of image data that provide rich visual information about ADLs than using other wearable sensors. In this paper, we explore the classification of ADLs from images captured by low temporal resolution wearable camera (2fpm) by using a Convolutional Neural Networks (CNN) approach. We show that the classification accuracy of a CNN largely improves when its output is combined, through a random decision forest, with contextual information from a fully connected layer. The proposed method was tested on a subset of the NTCIR-12 egocentric dataset, consisting of 18,674 images and achieved an overall accuracy of 86% activity recognition on 21 classes.

Connected through hyperedge HE-1 (induced by source paper "batch based activity recognition from egocentric photo streams revisited"):
  with members:
    - "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"
    - "predicting daily activities from egocentric images using deep learning"
    - "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"
    - "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"
    - "predicting daily activities from egocentric images using deep learning"

Connected through hyperedge HE-3 (induced by source paper "analyzing first person stories based on socializing eating and sedentary patterns"):
  with members:
    - "sentiment recognition in egocentric photostreams"
    - "toward storytelling from visual lifelogging an overview"
    - "sr clustering semantic regularized clustering for egocentric photo streams segmentation"
    - "with whom do i interact detecting social interactions in egocentric photo streams"
    - "simultaneous food localization and recognition"
    - "tensorflow a system for large scale machine learning"
    - "the fast convergence of incremental pca"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (induced by source paper "batch based activity recognition from egocentric photo streams"):
  with members:
    - "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"
    - "predicting daily activities from egocentric images using deep learning"
    - "very deep convolutional networks for large scale image recognition"
    - "long term recurrent convolutional networks for visual recognition and description"

Connected through hyperedge HE-5 (induced by source paper "detecting hands in egocentric videos towards action recognition"):
  with members:
    - "toward storytelling from visual lifelogging an overview"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "human daily activities indexing in videos from wearable cameras for monitoring of patients with dementia diseases"

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)