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: "parkinson s disease assessment from a wrist worn wearable sensor in free living conditions deep ensemble learning and visualization"
  Abstract: Parkinson's Disease (PD) is characterized by disorders in motor function such as freezing of gait, rest tremor, rigidity, and slowed and hyposcaled movements. Medication with dopaminergic medication may alleviate those motor symptoms, however, side-effects may include uncontrolled movements, known as dyskinesia. In this paper, an automatic PD motor-state assessment in free-living conditions is proposed using an accelerometer in a wrist-worn wearable sensor. In particular, an ensemble of convolutional neural networks (CNNs) is applied to capture the large variability of daily-living activities and overcome the dissimilarity between training and test patients due to the inter-patient variability. In addition, class activation map (CAM), a visualization technique for CNNs, is applied for providing an interpretation of the results.

Connected through hyperedge HE-1 (induced by source paper "a review of computational approaches for evaluation of rehabilitation exercises"):
  with members:
    - "a deep learning framework for assessing physical rehabilitation exercises"
    - "time series segmentation through automatic feature learning"
    - "shoulder physiotherapy exercise recognition machine learning the inertial signals from a smartwatch"
    - "using inertial sensors for position and orientation estimation"
    - "human motion analysis with deep metric learning"
    - "data augmentation of wearable sensor data for parkinson s disease monitoring using convolutional neural networks"
    - "exercise motion classification from large scale wearable sensor data using convolutional neural networks"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"

Connected through hyperedge HE-2 (induced by source paper "wearable based parkinson s disease severity monitoring using deep learning"):
  with members:
    - "deep learning for time series classification a review"
    - "wearable based mediation state detection in individuals with parkinson s disease"
    - "transfer learning for time series classification"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "distributed and parallel time series feature extraction for industrial big data applications"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "hand tremor frequency estimation in videos"):
  with members:
    - "real world repetition estimation by div grad and curl"
    - "video acceleration magnification"
    - "continuous video to simple signals for swimming stroke detection with convolutional neural networks"
    - "quasi periodicity quantification in video data using topology"
    - "convolutional pose machines"
    - "human pose estimation via convolutional part heatmap regression"
    - "making a case for learning motion representations with phase"
    - "deepcut joint subset partition and labeling for multi person pose estimation"

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)