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 convolutional and recurrent models for human activity recognition using wearables"
  Abstract: Human activity recognition (HAR) in ubiquitous computing is beginning to adopt deep learning to substitute for well-established analysis techniques that rely on hand-crafted feature extraction and classification techniques. From these isolated applications of custom deep architectures it is, however, difficult to gain an overview of their suitability for problems ranging from the recognition of manipulative gestures to the segmentation and identification of physical activities like running or ascending stairs. In this paper we rigorously explore deep, convolutional, and recurrent approaches across three representative datasets that contain movement data captured with wearable sensors. We describe how to train recurrent approaches in this setting, introduce a novel regularisation approach, and illustrate how they outperform the state-of-the-art on a large benchmark dataset. Across thousands of recognition experiments with randomly sampled model configurations we investigate the suitability of each model for different tasks in HAR, explore the impact of hyperparameters using the fANOVA framework, and provide guidelines for the practitioner who wants to apply deep learning in their problem setting.

Connected through hyperedge HE-1 (induced by source paper "an active smartphone authentication method based on daily cyclical activity"):
  with members:
    - "hmog new behavioral biometric features for continuous authentication of smartphone users"

Connected through hyperedge HE-2 (induced by source paper "riot enabling seamless context aware automation in the internet of things"):
  with members:
    - "context aware computing for the internet of things a survey"

Connected through hyperedge HE-3 (induced by source paper "multi task self supervised learning for human activity detection"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "deep learning for sensor based activity recognition a survey"
    - "unsupervised representation learning by predicting image rotations"
    - "universal language model fine tuning for text classification"
    - "protecting sensory data against sensitive inferences"
    - "cooperative learning of audio and video models from self supervised synchronization"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "audio visual scene analysis with self supervised multisensory features"

Connected through hyperedge HE-4 (induced by source paper "compressing rnns for iot devices by 15 38x using kronecker products"):
  with members:
    - "tucker tensor layer in fully connected neural networks"
    - "run time efficient rnn compression for inference on edge devices"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "measuring scheduling efficiency of rnns for nlp applications"
    - "the state of sparsity in deep neural networks"
    - "structured weight matrices based hardware accelerators in deep neural networks fpgas and asics"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"

Connected through hyperedge HE-5 (induced by source paper "memnet memory efficiency guided neural architecture search with augment trim learning"):
  with members:
    - "dvolver efficient pareto optimal neural network architecture search"
    - "ktan knowledge transfer adversarial network"
    - "you only search once single shot neural architecture search via direct sparse optimization"
    - "monas multi objective neural architecture search using reinforcement learning"
    - "wearable audio and imu based shot detection in racquet sports"
    - "efficient neural architecture search via parameter sharing"
    - "evolving deep neural networks"
    - "progressive neural architecture search"

Connected through hyperedge HE-6 (induced by source paper "run time efficient rnn compression for inference on edge devices"):
  with members:
    - "compressing rnns for iot devices by 15 38x using kronecker products"
    - "measuring scheduling efficiency of rnns for nlp applications"
    - "efficient recurrent neural networks using structured matrices in fpgas"
    - "structured weight matrices based hardware accelerators in deep neural networks fpgas and asics"
    - "alternating multi bit quantization for recurrent neural networks"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "learning compressed transforms with low displacement rank"
    - "block sparse recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "pushing the limits of rnn compression"):
  with members:
    - "ternary hybrid neural tree networks for highly constrained iot applications"
    - "run time efficient rnn compression for inference on edge devices"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "measuring scheduling efficiency of rnns for nlp applications"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "learning compressed transforms with low displacement rank"
    - "hello edge keyword spotting on microcontrollers"
    - "factorization tricks for lstm networks"

Connected through hyperedge HE-8 (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 recurrent neural network for mobile human activity recognition with high throughput"
    - "transition based dependency parsing with stack long short term memory"
    - "adam a method for stochastic optimization"
    - "how transferable are features in deep neural networks"

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)