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: "a multiple source framework for the identification of activities of daily living based on mobile device data"
  Abstract: The monitoring of the lifestyles may be performed based on a system for the recognition of Activities of Daily Living (ADL) and their environments, combining the results obtained with the user agenda. The system may be developed with the use of the off-the-shelf mobile devices commonly used, because they have several types of sensors available, including motion, magnetic, acoustic, and location sensors. Data acquisition, data processing, data fusion, and artificial intelligence methods are applied in different stages of the system developed, which recognizes the ADL with pattern recognition methods. The motion and magnetic sensors allow the recognition of activities with movement, but the acoustic sensors allow the recognition of the environments. The fusion of the motion, magnetic and acoustic sensors allows the differentiation of other ADL. On the other hand, the location sensors allows the recognition of ADL with large movement, and the combination of these sensors with the other sensors increases the number of ADL recognized by the system. This study consists on the comparison of different types of ANN for choosing the best methods for the recognition of several ADL, which they are implemented in a system for the recognition of ADL that combines the sensors data with the users agenda for the monitoring of the lifestyles. Conclusions point to the use of Deep Neural Networks (DNN) with normalized data for the identification of ADL with 85.89% of accuracy, the use of Feedforward neural networks with non-normalized data for the identification of the environments with 86.50% of accuracy, and the use of DNN with normalized data for the identification of standing activities with 100% of accuracy, proving the reliability of the framework presented in this study.

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

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)