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: "fast forward video based on semantic extraction"
  Abstract: Thanks to the low operational cost and large storage capacity of smartphones and wearable devices, people are recording many hours of daily activities, sport actions and home videos. These videos, also known as egocentric videos, are generally long-running streams with unedited content, which make them boring and visually unpalatable, bringing up the challenge to make egocentric videos more appealing. In this work we propose a novel methodology to compose the new fast-forward video by selecting frames based on semantic information extracted from images. The experiments show that our approach outperforms the state-of-the-art as far as semantic information is concerned and that it is also able to produce videos that are more pleasant to be watched.

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 "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-3 (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"
    - "discovering picturesque highlights from egocentric vacation videos"
    - "egosampling fast forward and stereo for egocentric videos"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-4 (induced by source paper "fast forwarding egocentric videos by listening and watching"):
  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"
    - "look listen and learn"
    - "towards semantic fast forward and stabilized egocentric videos"

Connected through hyperedge HE-5 (induced by source paper "semantic driven generation of hyperlapse from 360 circ video"):
  with members:
    - "pano2vid automatic cinematography for watching 360 circ videos"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "towards semantic fast forward and stabilized egocentric videos"):
  with members:
    - "a fast and accurate unconstrained face detector"
    - "egosampling wide view hyperlapse from single and multiple egocentric videos"
    - "compact cnn for indexing egocentric videos"
    - "egosampling fast forward and stereo for egocentric 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)