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: "on the importance and feasibility of forecasting data in sensors"
  Abstract: The first generation of wireless sensor nodes have constrained energy resources and computational power, which discourages applications to process any task other than measuring and transmitting towards a central server. However, nowadays, sensor networks tend to be incorporated into the Internet of Things and the hardware evolution may change the old strategy of avoiding data computation in the sensor nodes. In this paper, we show the importance of reducing the number of transmissions in sensor networks and present the use of forecasting methods as a way of doing it. Experiments using real sensor data show that state-of-the-art forecasting methods can be successfully implemented in the sensor nodes to keep the quality of their measurements and reduce up to 30% of their transmissions, lowering the channel utilization. We conclude that there is an old paradigm that is no longer the most beneficial, which is the strategy of always transmitting a measurement when it differs by more than a threshold from the last one transmitted. Adopting more complex forecasting methods in the sensor nodes is the alternative to significantly reduce the number of transmissions without compromising the quality of their measurements, and therefore support the exponential growth of the Internet of Things.

Connected through hyperedge HE-1 (induced by source paper "data aggregation techniques for internet of things"):
  with members:
    - "a federated filtering framework for internet of medical things"
    - "communication efficient learning of deep networks from decentralized data"
    - "a survey about prediction based data reduction in wireless sensor networks"
    - "robust pca via outlier pursuit"
    - "robust principal component analysis"

Connected through hyperedge HE-2 (induced by source paper "a federated filtering framework for internet of medical things"):
  with members:
    - "communication efficient learning of deep networks from decentralized data"
    - "a survey about prediction based data reduction in wireless sensor networks"

Connected through hyperedge HE-3 (induced by source paper "a survey about prediction based data reduction in wireless sensor networks"):
  with members:
    - "the impact of dual prediction schemes on the reduction of the number of transmissions in sensor networks"
    - "machine learning in wireless sensor networks algorithms strategies and applications"

Connected through hyperedge HE-4 (induced by source paper "a self managed architecture for sensor networks based on real time data analysis"):
  with members:
    - "a survey about prediction based data reduction in wireless sensor networks"

Connected through hyperedge HE-5 (induced by source paper "cloud empowered self managing wsns"):
  with members:
    - "a self managed architecture for sensor networks based on real time data analysis"

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)