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 new architecture of a ubiquitous health monitoring system a prototype of cloud mobile health monitoring system"
  Abstract: Abstract Wireless Body Area Sensor Networks (WBASN) is an emerging technology which uses wireless sensors to implement real-time wearable health monitoring of patients to enhance independent living. In this paper we propose a prototype of cloud mobile health monitoring system. The system uses WBASN and Smartphone application that uses cloud computing, location data and a neural network to determine the state of patients. Keywords: Mobile Cloud Computing, Mobile Health Monitoring, WBASN, Smartphone, Android, Python. 1.Introduction & Related Works Recent technological advances in sensors facilitate wireless sensor networks that are deeply embedded in their native environments. Wireless sensor networks are highly suitable for many applications, such as habitat monitoring [1], machine health monitoring and guidance, traffic pattern monitoring and navigation, plant monitoring in agriculture [2], and infrastructure monitoring. The current technological and economic trends enable new generations of wireless sensor networks with more compact and lighter sensor nodes, more processing power, and more storage capacity. In addition, the ongoing proliferation of wireless sensor networks across many application domains will result in a significant cost reduction. One of the most promising application domains is health monitoring [3], and within health care, WBANs in particular are emerging as promising enabling technologies to implement m-health. A WBASN for health monitoring consists of multiple sensor nodes that can measure and report the user’s physiological state. These sensors are strategically placed on the human body. The exact location of the sensor nodes on the human body depend on the sensor type, size and weight. Sensors can be worn as stand-alone devices or can be built into jewelry, applied as tiny patches on the skin, hidden in the user’s clothes or shoes, or even implanted in the user’s body. Each node in the WBASN is typically capable of sensing, sampling, processing, and wirelessly communicating one or more physiological signals. The exact number and type of physiological signals to be measured, processed, and reported, depends on end-user application and may include many physiological sensors (ECG, EMG, EEG, SPO2…). In addition to these sensors, a WBASN for health monitoring may include sensors that can help to determine the user’s location, discriminate among the user’s states (e.g., lying, sitting, walking, running), or estimate the type and level of the user’s physical activity. In this paper, we describe new Mobile Health Monitoring system architecture, it uses a wireless body area networks (WBASN) to collect and send data to the cloud server through GPRS/UMTS [4]. The system uses a cloud service to extract patient data information. These sensory parameters are fed into a neural network engine running as a cloud service that fuses information from multiple disparate sensors to determine whether the patient is in a "danger state". The merger of mobile computing and cloud resources is one of the popular areas of research today. There are several application including mobile social application [5], healthcare application [6, 7], gamming [8], and localization [9] that leverage cloud resources and machine learning algorithms to design efficient systems. The tradeoff lies in between energy consumption and computability, and in some cases best of both words can be leveraged. Choi et al. [10] proposed a system for ubiquitous health monitoring in the Bedroom via a Bluetooth Network and Wireless LAN. The information gathered from sensors connected to the patient's bed is transmitted to a monitoring station outside of the room where the data is processed and analyzed. Using the technologies of wireless body area networks (WBAN), Jovanov et al. [11] presented a Wearable health systems using WBAN for patient monitoring. The first level consists of physiological sensors, the second level is the personal server, and the third level is the health care servers and related services.

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.NI(Networking and Internet Architecture)