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: "performance improvement by introducing mobility in wireless communication networks"
  Abstract: Communication technology is a major contributor to our lifestyles. Improving the performance of communication system brings with various benefits to human beings. This report covers two typical such systems: wireless sensor networks and cellular networks. We study the influence of mobility in these networks. In wireless sensor networks, we consider the usage of mobile sinks to collect sensory data from static nodes. For the latter case, we design data collection protocols for a single mobile sink which aim at balancing the energy consumption among sensor nodes to improve the network lifetime. We also design an algorithm for multiple mobile sinks to collect urgent sensory information within the allowed latency. Regarding the mobility in cellular networks, we mean the service providers are mobile. Conventional approaches usually consider how to optimally deploy static base stations according to a certain metric, such as throughput. Base station densification is a solution, but it is not cost efficient. Introducing mobility is to let the base stations fly in the sky, resulting in less investment. We consider one of the key issues of using drones to serve cellular users: drone deployment. We have implemented all the proposed algorithms by either simulations or experiments, and the results have confirmed the effectiveness of these approaches.

Connected through hyperedge HE-1 (induced by source paper "sumo ss submodular optimization sensor scattering for deploying sensor networks by drones"):
  with members:
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "efficient informative sensing using multiple robots"

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)