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: "batch based activity recognition from egocentric photo streams revisited"
  Abstract: Wearable cameras can gather large amounts of image data that provide rich visual information about the daily activities of the wearer. Motivated by the large number of health applications that could be enabled by the automatic recognition of daily activities, such as lifestyle characterization for habit improvement, context-aware personal assistance and tele-rehabilitation services, we propose a system to classify 21 daily activities from photo-streams acquired by a wearable photo-camera. Our approach combines the advantages of a late fusion ensemble strategy relying on convolutional neural networks at image level with the ability of recurrent neural networks to account for the temporal evolution of high-level features in photo-streams without relying on event boundaries. The proposed batch-based approach achieved an overall accuracy of 89.85%, outperforming state-of-the-art end-to-end methodologies. These results were achieved on a dataset consists of 44,902 egocentric pictures from three persons captured during 26 days in average.

Connected through hyperedge HE-1 (induced by source paper "hierarchical approach to classify food scenes in egocentric photo streams"):
  with members:
    - "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"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"

Connected through hyperedge HE-2 (induced by source paper "social relation recognition in egocentric photostreams"):
  with members:
    - "person recognition in personal photo collections"
    - "a domain based approach to social relation recognition"
    - "toward storytelling from visual lifelogging an overview"
    - "sr clustering semantic regularized clustering for egocentric photo streams segmentation"
    - "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-3 (induced by source paper "unsupervised routine discovery in egocentric photo streams"):
  with members:
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "on the role of event boundaries in egocentric activity recognition from photostreams"):
  with members:
    - "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"
    - "predicting daily activities from egocentric images using deep learning"

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)