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: "towards semantic fast forward and stabilized egocentric videos"
  Abstract: The emergence of low-cost personal mobiles devices and wearable cameras and the increasing storage capacity of video-sharing websites have pushed forward a growing interest towards first-person videos. Since most of the recorded videos compose long-running streams with unedited content, they are tedious and unpleasant to watch. The fast-forward state-of-the-art methods are facing challenges of balancing the smoothness of the video and the emphasis in the relevant frames given a speed-up rate. In this work, we present a methodology capable of summarizing and stabilizing egocentric videos by extracting the semantic information from the frames. This paper also describes a dataset collection with several semantically labeled videos and introduces a new smoothness evaluation metric for egocentric videos that is used to test our method.

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"
    - "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"
    - "leaving some stones unturned dynamic feature prioritization for activity detection in streaming video"

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"
    - "fast forward video based on semantic extraction"
    - "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"
    - "fast forward video based on semantic extraction"

Connected through hyperedge HE-5 (induced by source paper "making a long story short a multi importance fast forwarding egocentric videos with the emphasis on relevant objects"):
  with members:
    - "egosampling wide view hyperlapse from egocentric videos"
    - "a fast and accurate unconstrained face detector"
    - "video summarization with long short term memory"

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)