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: "privacy preserving human activity recognition from extreme low resolution"
  Abstract: Privacy protection from surreptitious video recordings is an important societal challenge. We desire a computer vision system (e.g., a robot) that can recognize human activities and assist our daily life, yet ensure that it is not recording video that may invade our privacy. This paper presents a fundamental approach to address such contradicting objectives: human activity recognition while only using extreme low-resolution (e.g., 16x12) anonymized videos. We introduce the paradigm of inverse super resolution (ISR), the concept of learning the optimal set of image transformations to generate multiple low-resolution (LR) training videos from a single video. Our ISR learns different types of sub-pixel transformations optimized for the activity classification, allowing the classifier to best take advantage of existing high-resolution videos (e.g., YouTube videos) by creating multiple LR training videos tailored for the problem. We experimentally confirm that the paradigm of inverse super resolution is able to benefit activity recognition from extreme low-resolution videos.

Connected through hyperedge HE-1 (induced by source paper "an adversarial learning framework for preserving users anonymity in face based emotion recognition"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "a hybrid deep learning architecture for privacy preserving mobile analytics"
    - "minimax filter learning to preserve privacy from inference attacks"

Connected through hyperedge HE-2 (induced by source paper "deep poisoning functions towards robust privacy safe image data sharing"):
  with members:
    - "privacy preserving deep visual recognition an adversarial learning framework and a new dataset"
    - "the deepfake detection challenge dfdc preview dataset"
    - "training with the invisibles obfuscating images to share safely for learning visual recognition models"
    - "deepobfuscator adversarial training framework for privacy preserving image classification"
    - "privacy preserving image based localization"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "learning to anonymize faces for privacy preserving action detection"
    - "a hybrid model for identity obfuscation by face replacement"

Connected through hyperedge HE-3 (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-4 (induced by source paper "adversarial learning of privacy preserving and task oriented representations"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "learning privacy preserving encodings through adversarial training"
    - "knockoff nets stealing functionality of black box models"
    - "learning to anonymize faces for privacy preserving action detection"
    - "adversarial image perturbation for privacy protection a game theory perspective"
    - "faceless person recognition privacy implications in social media"
    - "deep variational information bottleneck"
    - "generating images with perceptual similarity metrics based on deep networks"

Connected through hyperedge HE-5 (induced by source paper "training with the invisibles obfuscating images to share safely for learning visual recognition models"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning to anonymize faces for privacy preserving action detection"
    - "a survey on data collection for machine learning a big data ai integration perspective"
    - "on classification of distorted images with deep convolutional neural networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "adversarial image perturbation for privacy protection a game theory perspective"
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"

Connected through hyperedge HE-6 (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-7 (induced by source paper "privacy preserving image based localization"):
  with members:
    - "revealing scenes by inverting structure from motion reconstructions"
    - "learning to anonymize faces for privacy preserving action detection"
    - "semantic visual localization"
    - "privacy preserving visual learning using doubly permuted homomorphic encryption"
    - "segmatch segment based place recognition in 3d point clouds"
    - "planet photo geolocation with convolutional neural networks"
    - "dsac differentiable ransac for camera localization"
    - "inverting visual representations with convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "deepobfuscator adversarial training framework for privacy preserving image classification"):
  with members:
    - "training with the invisibles obfuscating images to share safely for learning visual recognition models"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "better accuracy with quantified privacy representations learned via reconstructive adversarial network"
    - "privacy preserving deep inference for rich user data on the cloud"
    - "semantic image segmentation via deep parsing network"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "understanding deep image representations by inverting them"

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)