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 recurrent neural network for mobile human activity recognition with high throughput"
  Abstract: In this paper, we propose a method of human activity recognition with high throughput from raw accelerometer data applying a deep recurrent neural network (DRNN), and investigate various architectures and its combination to find the best parameter values. The "high throughput" refers to short time at a time of recognition. We investigated various parameters and architectures of the DRNN by using the training dataset of 432 trials with 6 activity classes from 7 people. The maximum recognition rate was 95.42% and 83.43% against the test data of 108 segmented trials each of which has single activity class and 18 multiple sequential trials, respectively. Here, the maximum recognition rates by traditional methods were 71.65% and 54.97% for each. In addition, the efficiency of the found parameters was evaluated by using additional dataset. Further, as for throughput of the recognition per unit time, the constructed DRNN was requiring only 1.347 [ms], while the best traditional method required 11.031 [ms] which includes 11.027 [ms] for feature calculation. These advantages are caused by the compact and small architecture of the constructed real time oriented DRNN.

Connected through hyperedge HE-1 (induced by source paper "few shot learning based human activity recognition"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "low shot learning with imprinted weights"
    - "prototypical networks for few shot learning"
    - "matching networks for one shot learning"
    - "deep convolutional and recurrent models for human activity recognition using wearables"
    - "transition based dependency parsing with stack long short term memory"
    - "adam a method for stochastic optimization"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "cross subject transfer learning in human activity recognition systems using generative adversarial networks"):
  with members:
    - "ensembles of deep lstm learners for activity recognition using wearables"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"

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"
    - "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-4 (induced by source paper "fullie and wiselie a dual stream recurrent convolutional attention model for activity recognition"):
  with members:
    - "ensembles of deep lstm learners for activity recognition using wearables"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "deep learning of representations looking forward"

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)