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: "multi face tracking by extended bag of tracklets in egocentric photo streams"
  Abstract: A method for multiface tracking in low frame rate egocentric videos is proposed (eBoT).eBoT generates a tracklet for each detected face and groups similar tracklets.eBoT extracts a prototype for each group of tracklets and estimates its confidence.eBoT is robust to drastic changes in location and appearance of faces.eBoT is robust to partial and severe occlusions and is able to localize them. Wearable cameras offer a hands-free way to record egocentric images of daily experiences, where social events are of special interest. The first step towards detection of social events is to track the appearance of multiple persons involved in them. In this paper, we propose a novel method to find correspondences of multiple faces in low temporal resolution egocentric videos acquired through a wearable camera. This kind of photo-stream imposes additional challenges to the multi-tracking problem with respect to conventional videos. Due to the free motion of the camera and to its low temporal resolution, abrupt changes in the field of view, in illumination condition and in the target location are highly frequent. To overcome such difficulties, we propose a multi-face tracking method that generates a set of tracklets through finding correspondences along the whole sequence for each detected face and takes advantage of the tracklets redundancy to deal with unreliable ones. Similar tracklets are grouped into the so called extended bag-of-tracklets (eBoT), which is aimed to correspond to a specific person. Finally, a prototype tracklet is extracted for each eBoT, where the occurred occlusions are estimated by relying on a new measure of confidence. We validated our approach over an extensive dataset of egocentric photo-streams and compared it to state of the art methods, demonstrating its effectiveness and robustness.

Connected through hyperedge HE-1 (induced by source paper "egoreid dataset person re identification in videos acquired by mobile devices with first person point of view"):
  with members:
    - "unsupervised traffic accident detection in first person videos"
    - "scaling egocentric vision the epic kitchens dataset"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "person search with natural language description"
    - "a pose sensitive embedding for person re identification with expanded cross neighborhood re ranking"
    - "beyond triplet loss a deep quadruplet network for person re identification"

Connected through hyperedge HE-2 (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"
    - "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"
    - "predicting daily activities from egocentric images using deep learning"
    - "sequence to sequence video to text"
    - "egocentric field of view localization using first person point of view devices"

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"
    - "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"
    - "seeing the big picture deep embedding with contextual evidences"

Connected through hyperedge HE-4 (induced by source paper "semantic summarization of egocentric photo stream events"):
  with members:
    - "r clustering for egocentric video segmentation"
    - "toward storytelling from visual lifelogging an overview"
    - "ego object discovery"
    - "end to end convolutional network for saliency prediction"
    - "visual summary of egocentric photostreams by representative keyframes"
    - "how transferable are features in deep neural networks"
    - "lsda large scale detection through adaptation"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "towards social pattern characterization in egocentric photo streams"):
  with members:
    - "all the people around me face discovery in egocentric photo streams"
    - "toward storytelling from visual lifelogging an overview"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"
    - "with whom do i interact detecting social interactions in egocentric photo streams"
    - "f formation detection individuating free standing conversational groups in images"
    - "very deep convolutional networks for large scale image recognition"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "social style characterization from egocentric photo streams"):
  with members:
    - "towards social pattern characterization in egocentric photo streams"
    - "all the people around me face discovery in egocentric photo streams"
    - "toward storytelling from visual lifelogging an overview"
    - "sr clustering semantic regularized clustering for egocentric photo streams segmentation"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"
    - "with whom do i interact detecting social interactions in egocentric photo streams"

Connected through hyperedge HE-7 (induced by source paper "all the people around me face discovery in egocentric photo streams"):
  with members:
    - "toward storytelling from visual lifelogging an overview"
    - "sr clustering semantic regularized clustering for egocentric photo streams segmentation"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-8 (induced by source paper "egocentric height estimation"):
  with members:
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "compact cnn for indexing egocentric videos"
    - "predicting important objects for egocentric video summarization"
    - "the evolution of first person vision methods a survey"
    - "an egocentric look at video photographer identity"
    - "two stream convolutional networks for action recognition in videos"

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)