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: "pattern recognition techniques for the identification of activities of daily living using mobile device accelerometer"
  Abstract: This paper focuses on the recognition of Activities of Daily Living (ADL) applying pattern recognition techniques to the data acquired by the accelerometer available in the mobile devices. The recognition of ADL is composed by several stages, including data acquisition, data processing, and artificial intelligence methods. The artificial intelligence methods used are related to pattern recognition, and this study focuses on the use of Artificial Neural Networks (ANN). The data processing includes data cleaning, and the feature extraction techniques to define the inputs for the ANN. Due to the low processing power and memory of the mobile devices, they should be mainly used to acquire the data, applying an ANN previously trained for the identification of the ADL. The main purpose of this paper is to present a new method implemented with ANN for the identification of a defined set of ADL with a reliable accuracy. This paper also presents a comparison of different types of ANN in order to choose the type for the implementation of the final method. Results of this research probes that the best accuracies are achieved with Deep Learning techniques with an accuracy higher than 80%.

Connected through hyperedge HE-1 (induced by source paper "an efficient machine learning based elderly fall detection algorithm"):
  with members:
    - "a multiple source framework for the identification of activities of daily living based on mobile device data"
    - "user environment detection with acoustic sensors embedded on mobile devices for the recognition of activities of daily living"

Connected through hyperedge HE-2 (induced by source paper "user environment detection with acoustic sensors embedded on mobile devices for the recognition of activities of daily living"):
  with members:
    - "data fusion on motion and magnetic sensors embedded on mobile devices for the identification of activities of daily living"
    - "sensingkit evaluating the sensor power consumption in ios devices"
    - "bigear inferring the ambient and emotional correlates from smartphone based acoustic big data"
    - "automatic environmental sound recognition performance versus computational cost"
    - "mobile device identification via sensor fingerprinting"

Connected through hyperedge HE-3 (induced by source paper "a multiple source framework for the identification of activities of daily living based on mobile device data"):
  with members:
    - "data fusion on motion and magnetic sensors embedded on mobile devices for the identification of activities of daily living"
    - "user environment detection with acoustic sensors embedded on mobile devices for the recognition of activities of daily living"
    - "sensingkit evaluating the sensor power consumption in ios devices"
    - "mobile device identification via sensor fingerprinting"

Connected through hyperedge HE-4 (induced by source paper "data fusion on motion and magnetic sensors embedded on mobile devices for the identification of activities of daily living"):
  with members:
    - "sensingkit evaluating the sensor power consumption in ios devices"
    - "mobile device identification via sensor fingerprinting"

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.CY(Computers and Society)