human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "labelsens enabling real time sensor data labelling at the point of collection on edge computing"):
  Abstract: In recent years, machine learning has made leaps and bounds enabling applications with high recognition accuracy for speech and images. However, other types of data to which these models can be applied have not yet been explored as thoroughly. In particular, it can be relatively challenging to accurately classify single or multi-model, real-time sensor data. Labelling is an indispensable stage of data pre-processing that can be even more challenging in real-time sensor data collection. Currently, real-time sensor data labelling is an unwieldly process with limited tools available and poor performance characteristics that can lead to the performance of the machine learning models being compromised. In this paper, we introduce new techniques for labelling at the point of collection coupled with a systematic performance comparison of two popular types of Deep Neural Networks running on five custom built edge devices. These state-of-the-art edge devices are designed to enable real-time labelling with various buttons, slide potentiometer and force sensors. This research provides results and insights that can help researchers utilising edge devices for real-time data collection select appropriate labelling techniques. We also identify common bottlenecks in each architecture and provide field tested guidelines to assist developers building adaptive, high performance edge solutions.

Includes members:
  Member "challenges of designing and developing tangible interfaces for mental well being":
    also connected through other hyperedges:
      (none)
  Member "notimind utilizing responses to smart phone notifications as affective sensors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beyond mobile apps a survey of technologies for mental well being")
  Member "comparative study of cnn and rnn for natural language processing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "benchmark performance of machine and deep learning based methodologies for urdu text document classification")
  Member "multi label music genre classification from audio text and images using deep features":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "machine learning for music genre multifaceted review and experimentation with audioset")
  Member "hollywood in homes crowdsourcing data collection for activity understanding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hallucinating statistical moment and subspace descriptors from object and saliency detectors for action recognition")
  Member "neural gpus learn algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "accessing higher level representations in sequential transformers with feedback memory")
  Member "empirical evaluation of gated recurrent neural networks on sequence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

Question: Please tell me which class the source paper 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)