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: "network assisted d2d relay selection under the presence of dynamic obstacles"
  Abstract: Millimeter wave (\texttt{mmWave}) channels in device to device (\texttt{D2D}) communication are susceptible to blockages in spite of using directional beams from multi-input multi-output (\texttt{MIMO}) antennas to compensate for high propagation loss. This motivates one to look for the presence of obstacles while forming \texttt{D2D} links among user equipments (\texttt{UEs}) which are in motion. In \texttt{D2D} communication, moving \texttt{UEs} also act as relays to forward data from one \texttt{UE} to another which introduces the problem of relay selection. The problem becomes more challenging when the obstacles are also in motion (dynamic obstacles) along with the moving \texttt{UEs}. First we have developed a probabilistic model for relay selection which considers both moving \texttt{UEs} and dynamic obstacles. Then we have analyzed the probability of dynamic obstacles blocking a link in 3D Euclidean space by exploiting the information from \texttt{MIMO} radar connected to the base station. Finally, using this information, we have developed unique strategies based on simple geometry to find the best relay which maximizes the expected data rate. Through simulations we have shown that our proposed strategy gives a significant improvement in packet loss due to mobility of nodes and dynamic obstacles in a \texttt{mmWave} channel over traditional approaches which do not consider dynamic obstacle's presence.

Connected through hyperedge HE-1 (induced by source paper "distributed relay selection in presence of dynamic obstacles in millimeter wave d2d communication"):
  with members:
    - "markov decision processes with applications in wireless sensor networks a survey"
    - "coverage and rate analysis for millimeter wave cellular networks"

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)