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: "classification driven dynamic image enhancement"
  Abstract: Convolutional neural networks rely on image texture and structure to serve as discriminative features to classify the image content. Image enhancement techniques can be used as preprocessing steps to help improve the overall image quality and in turn improve the overall effectiveness of a CNN. Existing image enhancement methods, however, are designed to improve the perceptual quality of an image for a human observer. In this paper, we are interested in learning CNNs that can emulate image enhancement and restoration, but with the overall goal to improve image classification and not necessarily human perception. To this end, we present a unified CNN architecture that uses a range of enhancement filters that can enhance image-specific details via end-to-end dynamic filter learning. We demonstrate the effectiveness of this strategy on four challenging benchmark datasets for fine-grained, object, scene, and texture classification: CUB-200-2011, PASCAL-VOC2007, MIT-Indoor, and DTD. Experiments using our proposed enhancement show promising results on all the datasets. In addition, our approach is capable of improving the performance of all generic CNN architectures.

Connected through hyperedge HE-1 (induced by source paper "dynamonet dynamic action and motion network"):
  with members:
    - "visual dynamics stochastic future generation via layered cross convolutional networks"
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "self supervised learning of face representations for video face clustering"
    - "learning human optical flow"
    - "spatio temporal channel correlation networks for action classification"
    - "cooperative learning of audio and video models from self supervised synchronization"
    - "d3d distilled 3d networks for video action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-2 (induced by source paper "bridging the gap between computational photography and visual recognition"):
  with members:
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "isaid a large scale dataset for instance segmentation in aerial images"
    - "report on ug 2 challenge track 1 assessing algorithms to improve video object detection and classification from unconstrained mobility platforms"
    - "single image deraining a comprehensive benchmark analysis"
    - "dual directed capsule network for very low resolution image recognition"
    - "vision meets drones a challenge"
    - "the perception distortion tradeoff"
    - "crafting a toolchain for image restoration by deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "transferable recognition aware image processing"):
  with members:
    - "segmentation aware image denoising without knowing true segmentation"
    - "report on ug 2 challenge track 1 assessing algorithms to improve video object detection and classification from unconstrained mobility platforms"
    - "bridging the gap between computational photography and visual recognition"
    - "disentangling features in 3d face shapes for joint face reconstruction and recognition"
    - "super identity convolutional neural network for face hallucination"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "task driven super resolution object detection in low resolution images"
    - "residual dense network for image super resolution"

Connected through hyperedge HE-4 (induced by source paper "report on ug 2 challenge track 1 assessing algorithms to improve video object detection and classification from unconstrained mobility platforms"):
  with members:
    - "bridging the gap between computational photography and visual recognition"
    - "vision meets drones a challenge"
    - "the unreasonable effectiveness of texture transfer for single image super resolution"
    - "yolov3 an incremental improvement"
    - "task driven super resolution object detection in low resolution images"
    - "learning to see in the dark"
    - "enhancing the performance of convolutional neural networks on quality degraded datasets"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-5 (induced by source paper "self supervised learning of face representations for video face clustering"):
  with members:
    - "unsupervised learning of face representations"
    - "from benedict cumberbatch to sherlock holmes character identification in tv series without a script"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "generating descriptions with grounded and co referenced people"
    - "a pose sensitive embedding for person re identification with expanded cross neighborhood re ranking"
    - "moviegraphs towards understanding human centric situations from videos"
    - "learning from video and text via large scale discriminative clustering"
    - "shuffle and learn unsupervised learning using temporal order verification"

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)