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: "extreme low resolution activity recognition with multi siamese embedding learning"
  Abstract: This paper presents an approach for recognizing human activities from extreme low resolution (e.g., 16x12) videos. Extreme low resolution recognition is not only necessary for analyzing actions at a distance but also is crucial for enabling privacy-preserving recognition of human activities. We design a new two-stream multi-Siamese convolutional neural network. The idea is to explicitly capture the inherent property of low resolution (LR) videos that two images originated from the exact same scene often have totally different pixel values depending on their LR transformations. Our approach learns the shared embedding space that maps LR videos with the same content to the same location regardless of their transformations. We experimentally confirm that our approach of jointly learning such transform robust LR video representation and the classifier outperforms the previous state-of-the-art low resolution recognition approaches on two public standard datasets by a meaningful margin.

Connected through hyperedge HE-1 (induced by source paper "are accelerometers for activity recognition a dead end"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "ensembles of deep lstm learners for activity recognition using wearables"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "the kinetics human action video dataset"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "action recognition with trajectory pooled deep convolutional descriptors"

Connected through hyperedge HE-2 (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"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-3 (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"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-4 (induced by source paper "privacy preserving action recognition using coded aperture videos"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "learning privacy preserving encodings through adversarial training"
    - "learning to anonymize faces for privacy preserving action detection"
    - "what have we learned from deep representations for action recognition"
    - "diffusercam lensless single exposure 3d imaging"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "playing for data ground truth from computer games"
    - "convolutional two stream network fusion for video action recognition"

Connected through hyperedge HE-5 (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"
    - "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"
    - "privacy preserving visual learning using doubly permuted homomorphic encryption"

Connected through hyperedge HE-6 (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"
    - "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"
    - "robust emotion recognition from low quality and low bit rate video a deep learning approach"

Connected through hyperedge HE-7 (induced by source paper "fully coupled two stream spatiotemporal networks for extremely low resolution action recognition"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "hidden two stream convolutional networks for action recognition"
    - "turn tap temporal unit regression network for temporal action proposals"
    - "privacy preserving human activity recognition from extreme low resolution"
    - "convolutional two stream network fusion for video action recognition"
    - "studying very low resolution recognition using deep networks"
    - "deep temporal linear encoding networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "privacy preserving action recognition for smart hospitals using low resolution depth images"):
  with members:
    - "fast and accurate single image super resolution via information distillation network"
    - "fully coupled two stream spatiotemporal networks for extremely low resolution action recognition"
    - "low resolution face recognition in the wild"
    - "towards vision based smart hospitals a system for tracking and monitoring hand hygiene compliance"
    - "a review of privacy and consent management in healthcare a focus on emerging data sources"
    - "fast and accurate image super resolution by deep cnn with skip connection and network in network"
    - "low resolution face recognition using a two branch deep convolutional neural network architecture"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"

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)