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: "towards vision based smart hospitals a system for tracking and monitoring hand hygiene compliance"
  Abstract: One in twenty-five patients admitted to a hospital will suffer from a hospital acquired infection. If we can intelligently track healthcare staff, patients, and visitors, we can better understand the sources of such infections. We envision a smart hospital capable of increasing operational efficiency and improving patient care with less spending. In this paper, we propose a non-intrusive vision-based system for tracking people's activity in hospitals. We evaluate our method for the problem of measuring hand hygiene compliance. Empirically, our method outperforms existing solutions such as proximity-based techniques and covert in-person observational studies. We present intuitive, qualitative results that analyze human movement patterns and conduct spatial analytics which convey our method's interpretability. This work is a first step towards a computer-vision based smart hospital and demonstrates promising results for reducing hospital acquired infections.

Connected through hyperedge HE-1 (induced by source paper "convolutional relational machine for group activity recognition"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "unsupervised learning of long term motion dynamics for videos"
    - "cern confidence energy recurrent network for group activity recognition"
    - "convolutional pose machines"
    - "social scene understanding end to end multi person action localization and collective activity recognition"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-2 (induced by source paper "a review of challenges and opportunities in machine learning for health"):
  with members:
    - "feature robustness in non stationary health records caveats to deployable model performance in common clinical machine learning tasks"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "adversarial attacks against medical deep learning systems"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "scalable and accurate deep learning for electronic health records"
    - "detecting hip fractures with radiologist level performance using deep neural networks"
    - "poincar e embeddings for learning hierarchical representations"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-3 (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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "graph distillation for action detection with privileged modalities"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "learning cross modal deep representations for robust pedestrian detection"
    - "temporal action detection with structured segment networks"
    - "investigation of different skeleton features for cnn based 3d action recognition"
    - "miml fcn multi instance multi label learning via fully convolutional networks with privileged information"
    - "unsupervised learning of long term motion dynamics for videos"
    - "joint distance maps based action recognition with convolutional neural 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)