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: "fully coupled two stream spatiotemporal networks for extremely low resolution action recognition"
  Abstract: A major emerging challenge is how to protect people's privacy as cameras and computer vision are increasingly integrated into our daily lives, including in smart devices inside homes. A potential solution is to capture and record just the minimum amount of information needed to perform a task of interest. In this paper, we propose a fully-coupled two-stream spatiotemporal architecture for reliable human action recognition on extremely low resolution (e.g., 12x16 pixel) videos. We provide an efficient method to extract spatial and temporal features and to aggregate them into a robust feature representation for an entire action video sequence. We also consider how to incorporate high resolution videos during training in order to build better low resolution action recognition models. We evaluate on two publicly-available datasets, showing significant improvements over the state-of-the-art.

Connected through hyperedge HE-1 (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"
    - "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"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-2 (induced by source paper "cross enhancement transform two stream 3d convnets for action recognition"):
  with members:
    - "hallucinating optical flow features for video classification"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "hidden two stream convolutional networks for action recognition"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "temporal convolutional networks for action segmentation and detection"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "long term temporal convolutions for action recognition"

Connected through hyperedge HE-3 (induced by source paper "temporal recurrent networks for online action detection"):
  with members:
    - "unsupervised traffic accident detection in first person videos"
    - "real world anomaly detection in surveillance videos"
    - "online action detection in untrimmed streaming videos modeling and evaluation"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "predictive corrective networks for action detection"

Connected through hyperedge HE-4 (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"
    - "low resolution face recognition in the wild"
    - "towards vision based smart hospitals a system for tracking and monitoring hand hygiene compliance"
    - "extreme low resolution activity recognition with multi siamese embedding learning"
    - "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)