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: "inserting videos into videos"
  Abstract: In this paper, we introduce a new problem of manipulating a given video by inserting other videos into it. Our main task is, given an object video and a scene video, to insert the object video at a user-specified location in the scene video so that the resulting video looks realistic. We aim to handle different object motions and complex backgrounds without expensive segmentation annotations. As it is difficult to collect training pairs for this problem, we synthesize fake training pairs that can provide helpful supervisory signals when training a neural network with unpaired real data. The proposed network architecture can take both real and fake pairs as input and perform both supervised and unsupervised training in an adversarial learning scheme. To synthesize a realistic video, the network renders each frame based on the current input and previous frames. Within this framework, we observe that injecting noise into previous frames while generating the current frame stabilizes training. We conduct experiments on real-world videos in object tracking and person re-identification benchmark datasets. Experimental results demonstrate that the proposed algorithm is able to synthesize long sequences of realistic videos with a given object video inserted.

Connected through hyperedge HE-1 (induced by source paper "object discovery with a copy pasting gan"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "monet unsupervised scene decomposition and representation"
    - "unsupervised moving object detection via contextual information separation"
    - "multi object representation learning with iterative variational inference"
    - "learning to generate synthetic data via compositing"
    - "unsupervised image matching and object discovery as optimization"
    - "learning to segment via cut and paste"
    - "learning to read by spelling towards unsupervised text recognition"

Connected through hyperedge HE-2 (induced by source paper "preserving semantic and temporal consistency for unpaired video to video translation"):
  with members:
    - "deep video inpainting"
    - "deep blind video decaptioning by temporal aggregation and recurrence"
    - "frame recurrent video super resolution"
    - "video to video synthesis"
    - "recycle gan unsupervised video retargeting"
    - "multimodal unsupervised image to image translation"
    - "learning blind video temporal consistency"
    - "adversarial scene editing automatic object removal from weak supervision"

Connected through hyperedge HE-3 (induced by source paper "improving the harmony of the composite image by spatial separated attention module"):
  with members:
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "selective kernel networks"
    - "semantic image synthesis with spatially adaptive normalization"
    - "temporally coherent video harmonization using adversarial networks"
    - "attention gan for object transfiguration in wild images"
    - "free form image inpainting with gated convolution"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "unsupervised attention guided image to image translation"

Connected through hyperedge HE-4 (induced by source paper "generative modeling for small data object detection"):
  with members:
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "versatile auxiliary classifier with generative adversarial network vac gan multi class scenarios"
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"
    - "context aware synthesis and placement of object instances"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "learning hierarchical semantic image manipulation through structured representations"
    - "unsupervised holistic image generation from key local patches"

Connected through hyperedge HE-5 (induced by source paper "align and attend network for globally and locally coherent video inpainting"):
  with members:
    - "deep blind video decaptioning by temporal aggregation and recurrence"
    - "video to video synthesis"
    - "learning blind video temporal consistency"
    - "free form image inpainting with gated convolution"
    - "youtube vos sequence to sequence video object segmentation"
    - "video inpainting by jointly learning temporal structure and spatial details"
    - "generative image inpainting with contextual attention"
    - "non local neural networks"

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)