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: "ego object discovery"
  Abstract: Lifelogging devices are spreading faster everyday. This growth can represent great benefits to develop methods for extraction of meaningful information about the user wearing the device and his/her environment. In this paper, we propose a semi-supervised strategy for easily discovering objects relevant to the person wearing a first-person camera. Given an egocentric video/images sequence acquired by the camera, our algorithm uses both the appearance extracted by means of a convolutional neural network and an object refill methodology that allows to discover objects even in case of small amount of object appearance in the collection of images. An SVM filtering strategy is applied to deal with the great part of the False Positive object candidates found by most of the state of the art object detectors. We validate our method on a new egocentric dataset of 4912 daily images acquired by 4 persons as well as on both PASCAL 2012 and MSRC datasets. We obtain for all of them results that largely outperform the state of the art approach. We make public both the EDUB dataset and the algorithm code.

Connected through hyperedge HE-1 (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"
    - "predicting daily activities from egocentric images using deep learning"
    - "sequence to sequence video to text"

Connected through hyperedge HE-2 (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"
    - "multi face tracking by extended bag of tracklets in egocentric photo streams"
    - "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-3 (induced by source paper "first person action object detection with egonet"):
  with members:
    - "holistically nested edge detection"
    - "high for low and low for high efficient boundary detection from deep object features and its applications to high level vision"
    - "attention to scale scale aware semantic image segmentation"
    - "semantic image segmentation with task specific edge detection using cnns and a discriminatively trained domain transform"
    - "exploiting egocentric object prior for 3d saliency detection"
    - "decoupled deep neural network for semi supervised semantic segmentation"
    - "predicting important objects for egocentric video summarization"
    - "learning deconvolution network for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "unsupervised learning of important objects from first person videos"):
  with members:
    - "first person action object detection with egonet"
    - "deep contrast learning for salient object detection"
    - "going deeper into first person activity recognition"
    - "scribblesup scribble supervised convolutional networks for semantic segmentation"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "unsupervised learning of edges"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"
    - "predicting important objects for egocentric video summarization"

Connected through hyperedge HE-5 (induced by source paper "exploiting egocentric object prior for 3d saliency detection"):
  with members:
    - "predicting important objects for egocentric video summarization"
    - "the secrets of salient object segmentation"
    - "fast edge detection using structured forests"

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)