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: "the impact of dual prediction schemes on the reduction of the number of transmissions in sensor networks"
  Abstract: Abstract   Future Internet of Things (IoT) applications will require that billions of wireless devices transmit data to the cloud frequently. However, the wireless medium access is pointed as a problem for the next generations of wireless networks; hence, the number of data transmissions in Wireless Sensor Networks (WSNs) can quickly become a bottleneck, disrupting the exponential growth in the number of interconnected devices, sensors, and amount of produced data. Therefore, keeping a low number of data transmissions is critical to incorporate new sensor nodes and measure a great variety of parameters in future generations of WSNs. Thanks to the high accuracy and low complexity of state-of-the-art forecasting algorithms, Dual Prediction Schemes (DPSs) are potential candidates to optimize the data transmissions in WSNs at the finest level because they facilitate for sensor nodes to avoid unnecessary transmissions without affecting the quality of their measurements. In this work, we present a sensor network model that uses statistical theorems to describe the expected impact of DPSs and data aggregation in WSNs. We aim to provide a foundation for future works by characterizing the theoretical gains of processing data in sensors and conditioning its transmission to the predictions’ accuracy. Our simulation results show that the number of transmissions can be reduced by almost 98% in the sensor nodes with the highest workload. We also detail the impact of predicting and aggregating transmissions according to the parameters that can be observed in common scenarios, such as sensor nodes’ transmission ranges, the correlation between measurements of different sensors, and the period between two consecutive measurements in a sensor.

Connected through hyperedge HE-1 (induced by source paper "a survey about prediction based data reduction in wireless sensor networks"):
  with members:
    - "on the importance and feasibility of forecasting data in sensors"
    - "machine learning in wireless sensor networks algorithms strategies and applications"

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)