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: "augmented coaching ecosystem for non obtrusive adaptive personalized elderly care on the basis of cloud fog dew computing paradigm"
  Abstract: The concept of the augmented coaching ecosystem for non-obtrusive adaptive personalized elderly care is proposed on the basis of the integration of new and available ICT approaches. They include multimodal user interface (MMUI), augmented reality (AR), machine learning (ML), Internet of Things (IoT), and machine-to-machine (M2M) interactions. The ecosystem is based on the Cloud-Fog-Dew computing paradigm services, providing a full symbiosis by integrating the whole range from low level sensors up to high level services using integration efficiency inherent in synergistic use of applied technologies. Inside of this ecosystem, all of them are encapsulated in the following network layers: Dew, Fog, and Cloud computing layer. Instead of the “spaghetti connections”, “mosaic of buttons”, “puzzles of output data”, etc., the proposed ecosystem provides the strict division in the following dataflow channels: consumer interaction channel, machine interaction channel, and caregiver interaction channel. This concept allows to decrease the physical, cognitive, and mental load on elderly care stakeholders by decreasing the secondary human-to-human (H2H), human-to-machine (H2M), and machine-to-human (M2H) interactions in favor of M2M interactions and distributed Dew Computing services environment. It allows to apply this non-obtrusive augmented reality ecosystem for effective personalized elderly care to preserve their physical, cognitive, mental and social well-being.

Connected through hyperedge HE-1 (induced by source paper "prediction of physical load level by machine learning analysis of heart activity after exercises"):
  with members:
    - "parallel statistical and machine learning methods for estimation of physical load"
    - "dnn driven speaker independent audio visual mask estimation for speech separation"
    - "user driven intelligent interface on the basis of multimodal augmented reality and brain computer interaction for people with functional disabilities"
    - "synergy of volunteer measurements and volunteer computing for effective data collecting processing simulating and analyzing on a worldwide scale"

Connected through hyperedge HE-2 (induced by source paper "user driven intelligent interface on the basis of multimodal augmented reality and brain computer interaction for people with functional disabilities"):
  with members:
    - "deep learning for fatigue estimation on the basis of multimodal human machine interactions"
    - "automatized generation of alphabets of symbols"
    - "generating and estimating nonverbal alphabets for situated and multimodal communications"
    - "performance analysis of open source machine learning frameworks for various parameters in single threaded and multi threaded modes"
    - "multi parametric statistical method for estimation of accumulated fatigue by sensors in ordinary gadgets"
    - "synergy of volunteer measurements and volunteer computing for effective data collecting processing simulating and analyzing on a worldwide scale"

Connected through hyperedge HE-3 (induced by source paper "open source dataset and machine learning techniques for automatic recognition of historical graffiti"):
  with members:
    - "capsule deep neural network for recognition of historical graffiti handwriting"
    - "user driven intelligent interface on the basis of multimodal augmented reality and brain computer interaction for people with functional disabilities"
    - "offline handwritten signature verification literature review"
    - "dynamic routing between capsules"
    - "synergy of volunteer measurements and volunteer computing for effective data collecting processing simulating and analyzing on a worldwide scale"

Connected through hyperedge HE-4 (induced by source paper "gamification for education of the digitally native generation by means of virtual reality augmented reality machine learning and brain computing interfaces in museums"):
  with members:
    - "deep learning for fatigue estimation on the basis of multimodal human machine interactions"
    - "user driven intelligent interface on the basis of multimodal augmented reality and brain computer interaction for people with functional disabilities"
    - "automatized generation of alphabets of symbols"
    - "music transcription by deep learning with data and artificial semantic augmentation"
    - "performance analysis of open source machine learning frameworks for various parameters in single threaded and multi threaded modes"

Connected through hyperedge HE-5 (induced by source paper "deep learning for fatigue estimation on the basis of multimodal human machine interactions"):
  with members:
    - "user driven intelligent interface on the basis of multimodal augmented reality and brain computer interaction for people with functional disabilities"
    - "comparative performance analysis of neural networks architectures on h2o platform for various activation functions"
    - "comparative analysis of open source frameworks for machine learning with use case in single threaded and multi threaded modes"
    - "synergy of volunteer measurements and volunteer computing for effective data collecting processing simulating and analyzing on a worldwide scale"

Connected through hyperedge HE-6 (induced by source paper "parallel statistical and machine learning methods for estimation of physical load"):
  with members:
    - "deep learning for fatigue estimation on the basis of multimodal human machine interactions"
    - "user driven intelligent interface on the basis of multimodal augmented reality and brain computer interaction for people with functional disabilities"
    - "synergy of volunteer measurements and volunteer computing for effective data collecting processing simulating and analyzing on a worldwide scale"

Connected through hyperedge HE-7 (induced by source paper "music transcription by deep learning with data and artificial semantic augmentation"):
  with members:
    - "user driven intelligent interface on the basis of multimodal augmented reality and brain computer interaction for people with functional disabilities"
    - "an experimental analysis of the entanglement problem in neural network based music transcription systems"
    - "automatized generation of alphabets of symbols"
    - "comparative performance analysis of neural networks architectures on h2o platform for various activation functions"
    - "slice to volume medical image registration a survey"
    - "comparative analysis of open source frameworks for machine learning with use case in single threaded and multi threaded modes"

Connected through hyperedge HE-8 (induced by source paper "performance analysis of open source machine learning frameworks for various parameters in single threaded and multi threaded modes"):
  with members:
    - "deep learning for fatigue estimation on the basis of multimodal human machine interactions"
    - "user driven intelligent interface on the basis of multimodal augmented reality and brain computer interaction for people with functional disabilities"
    - "automatized generation of alphabets of symbols"
    - "comparative performance analysis of neural networks architectures on h2o platform for various activation functions"
    - "comparative analysis of open source frameworks for machine learning with use case in single threaded and multi threaded modes"
    - "tensorflow a system for large scale machine learning"

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)