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: "mobile stride length estimation with deep convolutional neural networks"
  Abstract: Objective : Accurate estimation of spatial gait characteristics is critical to assess motor impairments resulting from neurological or musculoskeletal disease. Currently, however, methodological constraints limit clinical applicability of state-of-the-art double integration approaches to gait patterns with a clear zero-velocity phase.  Methods : We describe a novel approach to stride length estimation that uses deep convolutional neural networks to map stride-specific inertial sensor data to the resulting stride length. The model is trained on a publicly available and clinically relevant benchmark dataset consisting of 1220 strides from 101 geriatric patients. Evaluation is done in a tenfold cross validation and for three different stride definitions.  Results : Even though best results are achieved with strides defined from midstance to midstance with average accuracy and precision of   $\text{0.01} \pm \text{5.37}\,\text{cm}$   , performance does not strongly depend on stride definition. The achieved precision outperforms state-of-the-art methods evaluated on the same benchmark dataset by    $\boldsymbol{ 3.0\,\text{cm }(36\%)}$  .  Conclusion  : Due to the independence of stride definition, the proposed method is not subject to the methodological constrains that limit applicability of state-of-the-art double integration methods. Furthermore, it was possible to improve precision on the benchmark dataset.  Significance : With more precise mobile stride length estimation, new insights to the progression of neurological disease or early indications might be gained. Due to the independence of stride definition, previously uncharted diseases in terms of mobile gait analysis can now be investigated by retraining and applying the proposed method.

Connected through hyperedge HE-1 (induced by source paper "robust data driven zero velocity detection for foot mounted inertial navigation"):
  with members:
    - "ionet learning to cure the curse of drift in inertial odometry"
    - "lstm based zero velocity detection for robust inertial navigation"
    - "deep learning based speed estimation for constraining strapdown inertial navigation on smartphones"
    - "improving foot mounted inertial navigation through real time motion classification"
    - "backprop kf learning discriminative deterministic state estimators"
    - "a critical review of recurrent neural networks for sequence learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "lstm based zero velocity detection for robust inertial navigation"):
  with members:
    - "ionet learning to cure the curse of drift in inertial odometry"
    - "improving foot mounted inertial navigation through real time motion classification"
    - "data augmentation of wearable sensor data for parkinson s disease monitoring using convolutional neural networks"
    - "a critical review of recurrent neural networks for sequence learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "deepwalking enabling smartphone based walking speed estimation using deep learning"):
  with members:
    - "idnet smartphone based gait recognition with convolutional neural networks"
    - "enabling wifi p2p based pedestrian safety app"

Connected through hyperedge HE-4 (induced by source paper "sensor based gait parameter extraction with deep convolutional neural networks"):
  with members:
    - "deep convolutional and recurrent models for human activity recognition using wearables"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "show and tell a neural image caption generator"

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)