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: "semi coupled two stream fusion convnets for action recognition at extremely low resolutions"
  Abstract: Deep convolutional neural networks (ConvNets) have been recently shown to attain state-of-the-art performance for action recognition on standard-resolution videos. However, less attention has been paid to recognition performance at extremely low resolutions (eLR) (e.g., 16 x 12 pixels). Reliable action recognition using eLR cameras would address privacy concerns in various application environments such as private homes, hospitals, nursing/rehabilitation facilities, etc. In this paper, we propose a semi-coupled filter-sharing network that leverages high resolution (HR) videos during training in order to assist an eLR ConvNet. We also study methods for fusing spatial and temporal ConvNets customized for eLR videos in order to take advantage of appearance and motion information. Our method outperforms state-of-the-art methods at extremely low resolutions on IXMAS (93.7%) and HMDB (29.2%) datasets.

Connected through hyperedge HE-1 (induced by source paper "password conditioned anonymization and deanonymization with face identity transformers"):
  with members:
    - "cross view image synthesis using conditional gans"
    - "learning to anonymize faces for privacy preserving action detection"
    - "towards open set identity preserving face synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "a style based generator architecture for generative adversarial networks"
    - "ganimation anatomically aware facial animation from a single image"
    - "extreme low resolution activity recognition with multi siamese embedding learning"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-2 (induced by source paper "extreme low resolution activity recognition with spatial temporal attention transfer"):
  with members:
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "efficient video classification using fewer frames"
    - "low resolution face recognition in the wild via selective knowledge distillation"
    - "fully coupled two stream spatiotemporal networks for extremely low resolution action recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "extreme low resolution activity recognition with multi siamese embedding learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"

Connected through hyperedge HE-3 (induced by source paper "learning to anonymize faces for privacy preserving action detection"):
  with members:
    - "spatially transformed adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "extreme low resolution activity recognition with multi siamese embedding learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "sphereface deep hypersphere embedding for face recognition"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "learning latent super events to detect multiple activities in videos"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "towards privacy preserving visual recognition via adversarial training a pilot study"):
  with members:
    - "enhance visual recognition under adverse conditions via deep networks"
    - "extreme low resolution activity recognition with multi siamese embedding learning"
    - "towards a visual privacy advisor understanding and predicting privacy risks in images"
    - "learning transferable architectures for scalable image recognition"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "privacy preserving visual learning using doubly permuted homomorphic encryption"
    - "adversarial image perturbation for privacy protection a game theory perspective"

Connected through hyperedge HE-5 (induced by source paper "vgan based image representation learning for privacy preserving facial expression recognition"):
  with members:
    - "learning privacy preserving encodings through adversarial training"
    - "disentangled person image generation"
    - "disentangling factors of variation in deep representations using adversarial training"
    - "cryptoimg privacy preserving processing over encrypted images"
    - "semi supervised learning with deep generative models"
    - "discovering hidden factors of variation in deep networks"
    - "spontaneous expression classification in the encrypted domain"

Connected through hyperedge HE-6 (induced by source paper "privacy aware offloading of deep neural networks"):
  with members:
    - "learning to anonymize faces for privacy preserving action detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "privacy preserving human activity recognition from extreme low resolution"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "extreme low resolution activity recognition with multi siamese embedding learning"):
  with members:
    - "robust emotion recognition from low quality and low bit rate video a deep learning approach"
    - "privacy preserving human activity recognition from extreme low resolution"
    - "long term temporal convolutions for action recognition"
    - "studying very low resolution recognition using deep networks"
    - "end to end learning of action detection from frame glimpses in videos"
    - "unsupervised learning of visual representations using videos"
    - "deep residual learning for image recognition"
    - "learning spatiotemporal features with 3d convolutional 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)