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: "ffnet video fast forwarding via reinforcement learning"
  Abstract: For many applications with limited computation, communication, storage and energy resources, there is an imperative need of computer vision methods that could select an informative subset of the input video for efficient processing at or near real time. In the literature, there are two relevant groups of approaches: generating a trailer for a video or fast-forwarding while watching/processing the video. The first group is supported by video summarization techniques, which require processing of the entire video to select an important subset for showing to users. In the second group, current fast-forwarding methods depend on either manual control or automatic adaptation of playback speed, which often do not present an accurate representation and may still require processing of every frame. In this paper, we introduce FastForwardNet (FFNet), a reinforcement learning agent that gets inspiration from video summarization and does fast-forwarding differently. It is an online framework that automatically fast-forwards a video and presents a representative subset of frames to users on the fly. It does not require processing the entire video, but just the portion that is selected by the fast-forward agent, which makes the process very computationally efficient. The online nature of our proposed method also enables the users to begin fast-forwarding at any point of the video. Experiments on two real-world datasets demonstrate that our method can provide better representation of the input video with much less processing requirement.

Connected through hyperedge HE-1 (induced by source paper "video storytelling textual summaries for events"):
  with members:
    - "predicting visual features from text for image and video caption retrieval"
    - "unsupervised learning of view invariant action representations"
    - "query adaptive video summarization via quality aware relevance estimation"
    - "improved image captioning via policy gradient optimization of spider"
    - "dual glance model for deciphering social relationships"
    - "summarization of user generated sports video by using deep action recognition features"
    - "attention transfer from web images for video recognition"
    - "recurrent topic transition gan for visual paragraph generation"

Connected through hyperedge HE-2 (induced by source paper "pixelrl fully convolutional network with reinforcement learning for image processing"):
  with members:
    - "weakly supervised video summarization by hierarchical reinforcement learning"
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "chainer a deep learning framework for accelerating the research cycle"
    - "fully convolutional network with multi step reinforcement learning for image processing"
    - "distort and recover color enhancement using deep reinforcement learning"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "hierarchical cellular automata for visual saliency"

Connected through hyperedge HE-3 (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"
    - "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"
    - "data sets word embeddings learned from tweets and general data"

Connected through hyperedge HE-4 (induced by source paper "video summarisation by classification with deep reinforcement learning"):
  with members:
    - "dilated temporal relational adversarial network for generic video summarization"
    - "learning deep sketch abstraction"
    - "learning video summarization using unpaired data"
    - "discovering visual concept structure with sparse and incomplete tags"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "actor critic sequence training for image captioning"
    - "video captioning via hierarchical reinforcement learning"
    - "learning policies for adaptive tracking with deep feature cascades"

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)