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: "enhanced random access and beam training for mmwave wireless local networks with high user density"
  Abstract: As low frequency band becomes more and more crowded, millimeter-wave (mmWave) has attracted significant attention recently. IEEE has released the 802.11ad standard to satisfy the demand of ultra-high-speed communication. It adopts beamforming technology that can generate directional beams to compensate for high path loss. In the Association Beamforming Training (A-BFT) phase of beamforming (BF) training, a station (STA) randomly selects an A-BFT slot to contend for training opportunity. Due to the limited number of A-BFT slots, A-BFT phase suffers high probability of collisions in dense user scenarios, resulting in inefficient training performance. Based on the evaluation of the IEEE 802.11ad standard and 802.11ay draft in dense user scenarios of mmWave wireless networks, we propose an enhanced A-BFT beam training and random access mechanism, including the Separated A-BFT (SA-BFT) and Secondary Backoff A-BFT (SBA-BFT). The SA-BFT can provide more A-BFT slots and divide A-BFT slots into two regions by defining a new `E-A-BFT Length' field compared to the legacy 802.11ad A-BFT, thereby maintaining compatibility when 802.11ay devices are mixed with 802.11ad devices. It can also reduce the collision probability in dense user scenarios greatly. The SBA-BFT performs secondary backoff with very small overhead of transmission opportunities within one A-BFT slot, which not only further reduces collision probability, but also improves the A-BFT slots utilization. Furthermore, we propose a three-dimensional Markov model to analyze the performance of the SBA-BFT. The analytical and simulation results show that both the SA-BFT and the SBA-BFT can significantly improve BF training efficiency, which are beneficial to the optimization design of dense user wireless networks based on the IEEE 802.11ay standard and mmWave technology.

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)