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: "egosampling fast forward and stereo for egocentric videos"
  Abstract: While egocentric cameras like GoPro are gaining popularity, the videos they capture are long, boring, and difficult to watch from start to end. Fast forwarding (i.e. frame sampling) is a natural choice for faster video browsing. However, this accentuates the shake caused by natural head motion, making the fast forwarded video useless.

Connected through hyperedge HE-1 (induced by source paper "personalizing fast forward videos based on visual and textual features from social network"):
  with members:
    - "making a long story short a multi importance fast forwarding egocentric videos with the emphasis on relevant objects"
    - "a weighted sparse sampling and smoothing frame transition approach for semantic fast forward first person videos"
    - "ffnet video fast forwarding via reinforcement learning"
    - "video salient object detection via fully convolutional networks"
    - "egosampling wide view hyperlapse from egocentric videos"
    - "predicting visual features from text for image and video caption retrieval"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "query focused video summarization dataset evaluation and a memory network based approach"

Connected through hyperedge HE-2 (induced by source paper "mutual context network for jointly estimating egocentric gaze and actions"):
  with members:
    - "visual dynamics stochastic future generation via layered cross convolutional networks"
    - "digging deeper into egocentric gaze prediction"
    - "attention is all we need nailing down object centric attention for egocentric activity recognition"
    - "on the role of event boundaries in egocentric activity recognition from photostreams"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the kinetics human action video dataset"
    - "salgan visual saliency prediction with generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "video summarization using fully convolutional sequence networks"):
  with members:
    - "a general framework for edited video and raw video summarization"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "query focused video summarization dataset evaluation and a memory network based approach"
    - "convolutional sequence to sequence learning"
    - "collaborative summarization of topic related videos"
    - "temporal convolutional networks for action segmentation and detection"
    - "energy based generative adversarial network"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-4 (induced by source paper "ffnet video fast forwarding via reinforcement learning"):
  with members:
    - "egosampling wide view hyperlapse from egocentric videos"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "collaborative summarization of topic related videos"
    - "towards semantic fast forward and stabilized egocentric videos"
    - "poseagent budget constrained 6d object pose estimation via reinforcement learning"
    - "learning visual storylines with skipping recurrent neural networks"
    - "summary transfer exemplar based subset selection for video summarization"
    - "video summarization with long short term memory"

Connected through hyperedge HE-5 (induced by source paper "a weighted sparse sampling and smoothing frame transition approach for semantic fast forward first person videos"):
  with members:
    - "making a long story short a multi importance fast forwarding egocentric videos with the emphasis on relevant objects"
    - "egosampling wide view hyperlapse from egocentric videos"
    - "query focused video summarization dataset evaluation and a memory network based approach"
    - "collaborative summarization of topic related videos"
    - "towards semantic fast forward and stabilized egocentric videos"
    - "fast forward video based on semantic extraction"
    - "discovering picturesque highlights from egocentric vacation videos"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-6 (induced by source paper "discriminative feature learning for unsupervised video summarization"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "summary transfer exemplar based subset selection for video summarization"
    - "video summarization with long short term memory"
    - "unsupervised extraction of video highlights via robust recurrent auto encoders"
    - "going deeper with convolutions"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-7 (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"
    - "hierarchical structure and motion recovery from uncalibrated images"
    - "predicting daily activities from egocentric images using deep learning"
    - "orb slam a versatile and accurate monocular slam system"
    - "robust camera location estimation by convex programming"

Connected through hyperedge HE-8 (induced by source paper "computing egomotion with local loop closures for egocentric videos"):
  with members:
    - "compact cnn for indexing egocentric videos"
    - "orb slam a versatile and accurate monocular slam system"

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)