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: "discover and learn new objects from documentaries"
  Abstract: Despite the remarkable progress in recent years, detecting objects in a new context remains a challenging task. Detectors learned from a public dataset can only work with a fixed list of categories, while training from scratch usually requires a large amount of training data with detailed annotations. This work aims to explore a novel approach -- learning object detectors from documentary films in a weakly supervised manner. This is inspired by the observation that documentaries often provide dedicated exposition of certain object categories, where visual presentations are aligned with subtitles. We believe that object detectors can be learned from such a rich source of information. Towards this goal, we develop a joint probabilistic framework, where individual pieces of information, including video frames and subtitles, are brought together via both visual and linguistic links. On top of this formulation, we further derive a weakly supervised learning algorithm, where object model learning and training set mining are unified in an optimization procedure. Experimental results on a real world dataset demonstrate that this is an effective approach to learning new object detectors.

Connected through hyperedge HE-1 (induced by source paper "end to end learning of visual representations from uncurated instructional videos"):
  with members:
    - "a short note on the kinetics 700 human action dataset"
    - "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics"
    - "learning video representations using contrastive bidirectional transformer"
    - "contrastive multiview coding"
    - "mining youtube a dataset for learning fine grained action concepts from webly supervised video data"
    - "a case study on combining asr and visual features for generating instructional video captions"
    - "toward self supervised object detection in unlabeled videos"
    - "unsupervised pre training of image features on non curated data"

Connected through hyperedge HE-2 (induced by source paper "howto100m learning a text video embedding by watching hundred million narrated video clips"):
  with members:
    - "cross task weakly supervised learning from instructional videos"
    - "coin a large scale dataset for comprehensive instructional video analysis"
    - "scaling egocentric vision the epic kitchens dataset"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "unsupervised learning and segmentation of complex activities from video"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"
    - "a joint sequence fusion model for video question answering and retrieval"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-3 (induced by source paper "cap2det learning to amplify weak caption supervision for object detection"):
  with members:
    - "min entropy latent model for weakly supervised object detection"
    - "equal but not the same understanding the implicit relationship between persuasive images and text"
    - "neural baby talk"
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "deep learning for generic object detection a survey"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "pcl proposal cluster learning for weakly supervised object detection"
    - "artistic object recognition by unsupervised style adaptation"

Connected through hyperedge HE-4 (induced by source paper "learning to find common objects across few image collections"):
  with members:
    - "attention based deep multiple instance learning"
    - "prototypical networks for few shot learning"
    - "deep self taught learning for weakly supervised object localization"
    - "revisiting knowledge transfer for training object class detectors"
    - "few shot image recognition by predicting parameters from activations"
    - "multiple instance learning a survey of problem characteristics and applications"
    - "rapid adaptation with conditionally shifted neurons"
    - "weakly supervised object localization using things and stuff transfer"

Connected through hyperedge HE-5 (induced by source paper "optimizing video object detection via a scale time lattice"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "flow guided feature aggregation for video object detection"
    - "focal loss for dense object detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "detect to track and track to detect"
    - "dsod learning deeply supervised object detectors from scratch"

Connected through hyperedge HE-6 (induced by source paper "learning to discover and localize visual objects with open vocabulary"):
  with members:
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "deep learning for generic object detection a survey"
    - "learning to separate object sounds by watching unlabeled video"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "objects that sound"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "advise symbolism and external knowledge for decoding advertisements"

Connected through hyperedge HE-7 (induced by source paper "transferring agent behaviors from videos via motion gans"):
  with members:
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "feudal networks for hierarchical reinforcement learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "time contrastive networks self supervised learning from video"
    - "unsupervised learning for physical interaction through video prediction"
    - "deep visual foresight for planning robot motion"
    - "perceptual reward functions"
    - "cad2rl real single image flight without a single real image"

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)