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: "video summarization with long short term memory"
  Abstract: We propose a novel supervised learning technique for summarizing videos by automatically selecting keyframes or key subshots. Casting the problem as a structured prediction problem on sequential data, our main idea is to use Long Short-Term Memory (LSTM), a special type of recurrent neural networks to model the variable-range dependencies entailed in the task of video summarization. Our learning models attain the state-of-the-art results on two benchmark video datasets. Detailed analysis justifies the design of the models. In particular, we show that it is crucial to take into consideration the sequential structures in videos and model them. Besides advances in modeling techniques, we introduce techniques to address the need of a large number of annotated data for training complex learning models. There, our main idea is to exploit the existence of auxiliary annotated video datasets, albeit heterogeneous in visual styles and contents. Specifically, we show domain adaptation techniques can improve summarization by reducing the discrepancies in statistical properties across those datasets.

Connected through hyperedge HE-1 (induced by source paper "video storytelling textual summaries for events"):
  with members:
    - "ffnet video fast forwarding via reinforcement learning"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "summarynet a multi stage deep learning model for automatic video summarisation"):
  with members:
    - "cycle sum cycle consistent adversarial lstm networks for unsupervised video summarization"
    - "video summarization via actionness ranking"
    - "discriminative feature learning for unsupervised video summarization"
    - "video summarization using fully convolutional sequence networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "aenet learning deep audio features for video analysis"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised video summarization by hierarchical reinforcement learning"):
  with members:
    - "rethinking the evaluation of video summaries"
    - "fully convolutional network with multi step reinforcement learning for image processing"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "feudal networks for hierarchical reinforcement learning"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "deep reinforcement learning for visual object tracking in videos"
    - "deep reinforcement learning with a combinatorial action space for predicting popular reddit threads"

Connected through hyperedge HE-4 (induced by source paper "meta learning for task driven video summarization"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "a general framework for edited video and raw video summarization"
    - "patch alignment manifold matting"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "video summarization with attention based encoder decoder networks"
    - "meta sgd learning to learn quickly for few shot learning"
    - "collaborative summarization of topic related videos"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-5 (induced by source paper "multi modal deep analysis for multimedia"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "better and faster knowledge transfer from multiple self supervised learning tasks via graph distillation for video classification"
    - "visual question reasoning on general dependency tree"
    - "learning conditioned graph structures for interpretable visual question answering"
    - "sch gan semi supervised cross modal hashing by generative adversarial network"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "inferring and executing programs for visual reasoning"

Connected through hyperedge HE-6 (induced by source paper "scsampler sampling salient clips from video for efficient action recognition"):
  with members:
    - "video classification with channel separated convolutional networks"
    - "long term feature banks for detailed video understanding"
    - "learning discriminative video representations using adversarial perturbations"
    - "learning to separate object sounds by watching unlabeled video"
    - "adaframe adaptive frame selection for fast video recognition"
    - "slowfast networks for video recognition"
    - "the sound of pixels"
    - "cooperative learning of audio and video models from self supervised synchronization"

Connected through hyperedge HE-7 (induced by source paper "srg snippet relatedness based temporal action proposal generator"):
  with members:
    - "learning blind video temporal consistency"
    - "multi granularity generator for temporal action proposal"
    - "ctap complementary temporal action proposal generation"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "cbam convolutional block attention module"
    - "precise temporal action localization by evolving temporal proposals"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-8 (induced by source paper "synthetic defocus and look ahead autofocus for casual videography"):
  with members:
    - "sg fcn a motion and memory based deep learning model for video saliency detection"
    - "deeply supervised salient object detection with short connections"
    - "synthetic depth of field with a single camera mobile phone"
    - "deeplens shallow depth of field from a single image"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "scanner efficient video analysis at scale"
    - "tracking emerges by colorizing 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)