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: "data augmentation of wearable sensor data for parkinson s disease monitoring using convolutional neural networks"
  Abstract: While convolutional neural networks (CNNs) have been successfully applied to many challenging classification applications, they typically require large datasets for training. When the availability of labeled data is limited, data augmentation is a critical preprocessing step for CNNs. However, data augmentation for wearable sensor data has not been deeply investigated yet.   In this paper, various data augmentation methods for wearable sensor data are proposed. The proposed methods and CNNs are applied to the classification of the motor state of Parkinson’s Disease patients, which is challenging due to small dataset size, noisy labels, and large intra-class variability. Appropriate augmentation improves the classification performance from 77.54% to 86.88%.

Connected through hyperedge HE-1 (induced by source paper "robusttad robust time series anomaly detection via decomposition and convolutional neural networks"):
  with members:
    - "robustperiod time frequency mining for robust multiple periodicities detection"
    - "time series anomaly detection service at microsoft"
    - "deep semantic segmentation of natural and medical images a review"
    - "time series anomaly detection using convolutional neural networks and transfer learning"
    - "deeply supervised salient object detection with short connections"
    - "robusttrend a huber loss with a combined first and second order difference regularization for time series trend filtering"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "a deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data"

Connected through hyperedge HE-2 (induced by source paper "time series data augmentation for deep learning a survey"):
  with members:
    - "robusttad robust time series anomaly detection via decomposition and convolutional neural networks"
    - "robustperiod time frequency mining for robust multiple periodicities detection"
    - "augmenting physiological time series data a case study for sleep apnea detection"
    - "time series anomaly detection using convolutional neural networks and transfer learning"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "deep learning for anomaly detection a survey"
    - "learning data manipulation for augmentation and weighting"
    - "robusttrend a huber loss with a combined first and second order difference regularization for time series trend filtering"

Connected through hyperedge HE-3 (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"
    - "parkinson s disease assessment from a wrist worn wearable sensor in free living conditions deep ensemble learning and visualization"
    - "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"
    - "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-4 (induced by source paper "multi task self supervised learning for human activity detection"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "deep learning for sensor based activity recognition a survey"
    - "unsupervised representation learning by predicting image rotations"
    - "universal language model fine tuning for text classification"
    - "protecting sensory data against sensitive inferences"
    - "cooperative learning of audio and video models from self supervised synchronization"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "audio visual scene analysis with self supervised multisensory features"

Connected through hyperedge HE-5 (induced by source paper "time series anomaly detection using convolutional neural networks and transfer learning"):
  with members:
    - "automatic salt deposits segmentation a deep learning approach"
    - "exploring the limits of weakly supervised pretraining"
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "automatic brain tumor detection and segmentation using u net based fully convolutional networks"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "multi scale convolutional neural networks for time series classification"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "multivariate industrial time series with cyber attack simulation fault detection using an lstm based predictive data model"

Connected through hyperedge HE-6 (induced by source paper "learning bodily and temporal attention in protective movement behavior detection"):
  with members:
    - "automatic detection of protective behavior in chronic pain physical rehabilitation a recurrent neural network approach"
    - "understanding and improving recurrent networks for human activity recognition by continuous attention"
    - "a human action descriptor based on motion coordination"
    - "deepsense a unified deep learning framework for time series mobile sensing data processing"
    - "deep convolutional and recurrent models for human activity recognition using wearables"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "kinetic song comprehension deciphering personal listening habits via phone vibrations"):
  with members:
    - "spearphone a speech privacy exploit via accelerometer sensed reverberations from smartphone loudspeakers"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "we can track you if you take the metro tracking metro riders using accelerometers on smartphones"
    - "touchalytics on the applicability of touchscreen input as a behavioral biometric for continuous authentication"

Connected through hyperedge HE-8 (induced by source paper "quantitative phase imaging and artificial intelligence a review"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "virtual histological staining of unlabelled tissue autofluorescence images via deep learning"
    - "extended depth of field in holographic imaging using deep learning based autofocusing and phase recovery"
    - "phase recovery and holographic image reconstruction using deep learning in neural networks"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "choicenet robust learning by revealing output correlations"
    - "deep learning enhanced mobile phone microscopy"
    - "attention u net learning where to look for the pancreas"

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)