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: "deepsense a unified deep learning framework for time series mobile sensing data processing"
  Abstract: Mobile sensing applications usually require time-series inputs from sensors. Some applications, such as tracking, can use sensed acceleration and rate of rotation to calculate displacement based on physical system models. Other applications, such as activity recognition, extract manually designed features from sensor inputs for classification. Such applications face two challenges. On one hand, on-device sensor measurements are noisy. For many mobile applications, it is hard to find a distribution that exactly describes the noise in practice. Unfortunately, calculating target quantities based on physical system and noise models is only as accurate as the noise assumptions. Similarly, in classification applications, although manually designed features have proven to be effective, it is not always straightforward to find the most robust features to accommodate diverse sensor noise patterns and user behaviors. To this end, we propose DeepSense, a deep learning framework that directly addresses the aforementioned noise and feature customization challenges in a unified manner. DeepSense integrates convolutional and recurrent neural networks to exploit local interactions among similar mobile sensors, merge local interactions of different sensory modalities into global interactions, and extract temporal relationships to model signal dynamics. DeepSense thus provides a general signal estimation and classification framework that accommodates a wide range of applications. We demonstrate the effectiveness of DeepSense using three representative and challenging tasks: car tracking with motion sensors, heterogeneous human activity recognition, and user identification with biometric motion analysis. DeepSense significantly outperforms the state-of-the-art methods for all three tasks. In addition, DeepSense is feasible to implement on smartphones due to its moderate energy consumption and low latency

Connected through hyperedge HE-1 (induced by source paper "patdnn achieving real time dnn execution on mobile devices with pattern based weight pruning"):
  with members:
    - "deep learning on mobile devices a review"
    - "progressive dnn compression a key to achieve ultra high weight pruning and quantization rates using admm"
    - "pconv the missing but desirable sparsity in dnn weight pruning for real time execution on mobile devices"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deepcache principled cache for mobile deep vision"
    - "admm nn an algorithm hardware co design framework of dnns using alternating direction method of multipliers"
    - "amc automl for model compression and acceleration on mobile devices"
    - "tvm an automated end to end optimizing compiler for deep learning"

Connected through hyperedge HE-2 (induced by source paper "an image enhancing pattern based sparsity for real time inference on mobile devices"):
  with members:
    - "autoslim towards one shot architecture search for channel numbers"
    - "2pfpce two phase filter pruning based on conditional entropy"
    - "deepcache principled cache for mobile deep vision"
    - "amc automl for model compression and acceleration on mobile devices"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "discrimination aware channel pruning for deep neural networks"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-3 (induced by source paper "a deep learning framework for assessing physical rehabilitation exercises"):
  with members:
    - "a fine to coarse convolutional neural network for 3d human action recognition"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "dynamic temporal pyramid network a closer look at multi scale modeling for activity detection"
    - "temporal action detection with structured segment networks"
    - "deep representation learning for human motion prediction and classification"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "exercise motion classification from large scale wearable sensor data using convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "mobility inference on long tailed sparse trajectory"):
  with members:
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-5 (induced by source paper "pconv the missing but desirable sparsity in dnn weight pruning for real time execution on mobile devices"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "making convolutional networks shift invariant again"
    - "2pfpce two phase filter pruning based on conditional entropy"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "deepcache principled cache for mobile deep vision"
    - "admm nn an algorithm hardware co design framework of dnns using alternating direction method of multipliers"
    - "amc automl for model compression and acceleration on mobile devices"
    - "tvm an automated end to end optimizing compiler for deep learning"

Connected through hyperedge HE-6 (induced by source paper "constructing energy efficient mixed precision neural networks through principal component analysis for edge intelligence"):
  with members:
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "per tensor fixed point quantization of the back propagation algorithm"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "a low effort approach to structured cnn design using pca"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "hybrid binary networks optimizing for accuracy efficiency and memory"
    - "mixed precision quantization of convnets via differentiable neural architecture search"
    - "pact parameterized clipping activation for quantized neural networks"

Connected through hyperedge HE-7 (induced by source paper "deep learning acceleration techniques for real time mobile vision applications"):
  with members:
    - "convolutional neural network for intrusion detection system in cyber physical systems"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "towards high performance video object detection for mobiles"
    - "ai benchmark running deep neural networks on android smartphones"
    - "towards a theoretical understanding of hashing based neural nets"
    - "tiny ssd a tiny single shot detection deep convolutional neural network for real time embedded object detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep reinforcement learning for visual object tracking in videos"

Connected through hyperedge HE-8 (induced by source paper "stfnets learning sensing signals from the time frequency perspective with short time fourier neural networks"):
  with members:
    - "fastdeepiot towards understanding and optimizing neural network execution time on mobile and embedded devices"
    - "deep complex networks"
    - "deepiot compressing deep neural network structures for sensing systems with a compressor critic framework"
    - "rdeepsense reliable deep mobile computing models with uncertainty estimations"
    - "semi supervised classification with graph convolutional networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"

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)