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: "deep activity recognition models with triaxial accelerometers"
  Abstract: Despite the widespread installation of accelerometers in almost all mobile phones and wearable devices, activity recognition using accelerometers is still immature due to the poor recognition accuracy of existing recognition methods and the scarcity of labeled training data. We consider the problem of human activity recognition using triaxial accelerometers and deep learning paradigms. This paper shows that deep activity recognition models (a) provide better recognition accuracy of human activities, (b) avoid the expensive design of handcrafted features in existing systems, and (c) utilize the massive unlabeled acceleration samples for unsupervised feature extraction. Moreover, a hybrid approach of deep learning and hidden Markov models (DL-HMM) is presented for sequential activity recognition. This hybrid approach integrates the hierarchical representations of deep activity recognition models with the stochastic modeling of temporal sequences in the hidden Markov models. We show substantial recognition improvement on real world datasets over state-of-the-art methods of human activity recognition using triaxial accelerometers.

Connected through hyperedge HE-1 (induced by source paper "asymmetric residual neural network for accurate human activity recognition"):
  with members:
    - "deep attention guided hashing"
    - "dfternet towards 2 bit dynamic fusion networks for accurate human activity recognition"
    - "slowfast networks for video recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "compression artifacts removal using convolutional neural networks"
    - "a theory of local learning the learning channel and the optimality of backpropagation"
    - "spatiotemporal residual networks for video action recognition"

Connected through hyperedge HE-2 (induced by source paper "landscape of big medical data a pragmatic survey on prioritized tasks"):
  with members:
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "scalable and accurate deep learning for electronic health records"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "adversarial deep structured nets for mass segmentation from mammograms"
    - "a survey on deep learning in medical image analysis"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "a unified framework for tumor proliferation score prediction in breast histopathology"
    - "atomnet a deep convolutional neural network for bioactivity prediction in structure based drug discovery"

Connected through hyperedge HE-3 (induced by source paper "leveraging machine learning and big data for smart buildings a comprehensive survey"):
  with members:
    - "ensembles of deep lstm learners for activity recognition using wearables"
    - "unsupervised machine learning for networking techniques applications and research challenges"
    - "machine learning for internet of things data analysis a survey"
    - "microlocation for internet of things equipped smart buildings"
    - "context aware computing for the internet of things a survey"
    - "regression with linear factored functions"

Connected through hyperedge HE-4 (induced by source paper "sparsesense human activity recognition from highly sparse sensor data streams using set based neural networks"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "deep sets"
    - "ensembles of deep lstm learners for activity recognition using wearables"

Connected through hyperedge HE-5 (induced by source paper "dfternet towards 2 bit dynamic fusion networks for accurate human activity recognition"):
  with members:
    - "learning attribute representation for human activity recognition"
    - "faster population counts using avx2 instructions"
    - "network sketching exploiting binary structure in deep cnns"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "qsgd communication efficient sgd via gradient quantization and encoding"

Connected through hyperedge HE-6 (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"
    - "sensor based gait parameter extraction with deep convolutional neural networks"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "understanding and improving recurrent networks for human activity recognition by continuous attention"):
  with members:
    - "semi supervised convolutional neural networks for human activity recognition"
    - "influence directed explanations for deep convolutional networks"
    - "ensembles of deep lstm learners for activity recognition using wearables"
    - "neural machine translation and sequence to sequence models a tutorial"
    - "deepsense a unified deep learning framework for time series mobile sensing data processing"
    - "deep convolutional and recurrent models for human activity recognition using wearables"
    - "show attend and tell neural image caption generation with visual attention"
    - "attention based models for speech recognition"

Connected through hyperedge HE-8 (induced by source paper "learning behavioral context recognition with multi stream temporal convolutional networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "depthwise separable convolutions for neural machine translation"
    - "recognizing detailed human context in the wild from smartphones and smartwatches"
    - "xception deep learning with depthwise separable convolutions"
    - "deep convolutional and recurrent models for human activity recognition using wearables"

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)