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: "sensor based gait parameter extraction with deep convolutional neural networks"
  Abstract: Measurement of stride-related, biomechanical parameters is the common rationale for objective gait impairment scoring. State-of-the-art double-integration approaches to extract these parameters from inertial sensor data are, however, limited in their clinical applicability due to the underlying assumptions. To overcome this, we present a method to translate the abstract information provided by wearable sensors to context-related expert features based on deep convolutional neural networks. Regarding mobile gait analysis, this enables integration-free and data-driven extraction of a set of eight spatio-temporal stride parameters. To this end, two modeling approaches are compared: a combined network estimating all parameters of interest and an ensemble approach that spawns less complex networks for each parameter individually. The ensemble approach is outperforming the combined modeling in the current application. On a clinically relevant and publicly available benchmark dataset, we estimate stride length, width and medio-lateral change in foot angle up to -0.15 ± 6.09 cm, -0.09 ± 4.22 cm and 0.13 ± 3.78° respectively. Stride, swing and stance time as well as heel and toe contact times are estimated up to ±0.07, ±0.05, ±0.07, ±0.07 and ±0.12 s respectively. This is comparable to and in parts outperforming or defining state of the art. Our results further indicate that the proposed change in the methodology could substitute assumption-driven double-integration methods and enable mobile assessment of spatio-temporal stride parameters in clinically critical situations as, e.g., in the case of spastic gait impairments.

Connected through hyperedge HE-1 (induced by source paper "sensor fusion for predictive control of human prosthesis environment dynamics in assistive walking a survey"):
  with members:
    - "directional pointnet 3d environmental classification for wearable robotics"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-2 (induced by source paper "deep inertial poser learning to reconstruct human pose from sparse inertial measurements in real time"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "video based reconstruction of 3d people models"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "doublefusion real time capture of human performances with inner body shapes from a single depth sensor"
    - "learning human motion models for long term predictions"
    - "single shot multi person 3d pose estimation from monocular rgb"
    - "end to end recovery of human shape and pose"
    - "towards accurate markerless human shape and pose estimation over time"

Connected through hyperedge HE-3 (induced by source paper "deep learning for sensor based activity recognition a survey"):
  with members:
    - "balanced distribution adaptation for transfer learning"
    - "a deep learning framework using passive wifi sensing for respiration monitoring"
    - "ensembles of deep lstm learners for activity recognition using wearables"
    - "transforming sensor data to the image domain for deep learning an application to footstep detection"
    - "deepsense a unified deep learning framework for time series mobile sensing data processing"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "deep convolutional and recurrent models for human activity recognition using wearables"
    - "deep recurrent neural network for mobile human activity recognition with high throughput"

Connected through hyperedge HE-4 (induced by source paper "vision based assessment of parkinsonism and levodopa induced dyskinesia with pose estimation"):
  with members:
    - "convolutional pose machines"
    - "sparseness meets deepness 3d human pose estimation from monocular video"
    - "articulated pose estimation by a graphical model with image dependent pairwise relations"
    - "deeppose human pose estimation via deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "automatic classification of functional gait disorders"):
  with members:
    - "representation learning a review and new perspectives"
    - "document clustering evaluation divergence from a random baseline"

Connected through hyperedge HE-6 (induced by source paper "sparse inertial poser automatic 3d human pose estimation from sparse imus"):
  with members:
    - "keep it smpl automatic estimation of 3d human pose and shape from a single image"

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.LG(Machine Learning)