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: "ensembles of deep lstm learners for activity recognition using wearables"
  Abstract: Recently, deep learning (DL) methods have been introduced very successfully into human activity recognition (HAR) scenarios in ubiquitous and wearable computing. Especially the prospect of overcoming the need for manual feature design combined with superior classification capabilities render deep neural networks very attractive for real-life HAR applications. Even though DL-based approaches now outperform the state-of-the-art in a number of recognition tasks, still substantial challenges remain. Most prominently, issues with real-life datasets, typically including imbalanced datasets and problematic data quality, still limit the effectiveness of activity recognition using wearables. In this paper we tackle such challenges through Ensembles of deep Long Short Term Memory (LSTM) networks. LSTM networks currently represent the state-of-the-art with superior classification performance on relevant HAR benchmark datasets. We have developed modified training procedures for LSTM networks and combine sets of diverse LSTM learners into classifier collectives. We demonstrate that Ensembles of deep LSTM learners outperform individual LSTM networks and thus push the state-of-the-art in human activity recognition using wearables. Through an extensive experimental evaluation on three standard benchmarks (Opportunity, PAMAP2, Skoda) we demonstrate the excellent recognition capabilities of our approach and its potential for real-life applications of human activity recognition.

Connected through hyperedge HE-1 (induced by source paper "are accelerometers for activity recognition a dead end"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "extreme low resolution activity recognition with multi siamese embedding learning"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "the kinetics human action video dataset"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "action recognition with trajectory pooled deep convolutional descriptors"

Connected through hyperedge HE-2 (induced by source paper "order matters shuffling sequence generation for video prediction"):
  with members:
    - "towards universal representation for unseen action recognition"
    - "stochastic adversarial video prediction"
    - "robust cross view gait identification with evidence a discriminant gait gan diggan approach on 10000 people"
    - "learning to decompose and disentangle representations for video prediction"
    - "visual robot task planning"
    - "decomposing motion and content for natural video sequence prediction"
    - "folded recurrent neural networks for future video prediction"
    - "desire distant future prediction in dynamic scenes with interacting agents"

Connected through hyperedge HE-3 (induced by source paper "leveraging machine learning and big data for smart buildings a comprehensive survey"):
  with members:
    - "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"
    - "deep activity recognition models with triaxial accelerometers"
    - "context aware computing for the internet of things a survey"
    - "regression with linear factored functions"

Connected through hyperedge HE-4 (induced by source paper "automatic detection of protective behavior in chronic pain physical rehabilitation a recurrent neural network approach"):
  with members:
    - "multi instance dynamic ordinal random fields for weakly supervised facial behavior analysis"
    - "regularizing face verification nets for pain intensity regression"
    - "deep convolutional and recurrent models for human activity recognition using wearables"
    - "action recognition with trajectory pooled deep convolutional descriptors"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "cross subject transfer learning in human activity recognition systems using generative adversarial networks"):
  with members:
    - "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"
    - "deep recurrent neural network for mobile human activity recognition with high throughput"

Connected through hyperedge HE-6 (induced by source paper "multi agent attentional activity recognition"):
  with members:
    - "multi modality sensor data classification with selective attention"
    - "interpretable parallel recurrent neural networks with convolutional attentions for multi modality activity modeling"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "recurrent models of visual attention"

Connected through hyperedge HE-7 (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"
    - "deep activity recognition models with triaxial accelerometers"

Connected through hyperedge HE-8 (induced by source paper "towards reliable automated general movement assessment for perinatal stroke screening in infants using wearable accelerometers"):
  with members:
    - "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)