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: "ap initiated multi user transmissions in ieee 802 11ax wlans"
  Abstract: Next-generation 802.11ax WLANs will make extensive use of multi-user communications in both downlink (DL) and uplink (UL) directions to achieve high and efficient spectrum utilization in scenarios with many user stations per access point. It will become possible with the support of multi-user (MU) multiple input, multiple output (MIMO) and orthogonal frequency division multiple access (OFDMA) transmissions. In this paper, we first overview the novel characteristics introduced by IEEE 802.11ax to implement AP-initiated OFDMA and MU-MIMO transmissions in both downlink and uplink directions. Namely, we describe the changes made at the physical layer and at the medium access control layer to support OFDMA, the use of \emph{trigger frames} to schedule uplink multi-user transmissions, and the new \emph{multi-user RTS/CTS mechanism} to protect large multi-user transmissions from collisions. Then, in order to study the achievable throughput of an 802.11ax network, we use both mathematical analysis and simulations to numerically quantify the benefits of MU transmissions and the impact of 802.11ax overheads on the WLAN saturation throughput. Results show the advantages of MU transmissions in scenarios with many user stations, also providing some novel insights on the conditions in which 802.11ax WLANs are able to maximize their performance, such as the existence of an optimal number of active user stations in terms of throughput, or the need to provide strict prioritization to AP-initiated MU transmissions to avoid collisions with user stations.

Connected through hyperedge HE-1 (induced by source paper "contention window optimization in ieee 802 11ax networks with deep reinforcement learning"):
  with members:
    - "a flexible machine learning aware architecture for future wlans"
    - "a collaborative multi agent reinforcement learning anti jamming algorithm in wireless networks"
    - "deep learning in mobile and wireless networking a survey"
    - "deep reinforcement learning multiple access for heterogeneous wireless networks"
    - "control theoretic optimization of 802 11 wlans implementation and experimental evaluation"

Connected through hyperedge HE-2 (induced by source paper "target wake time scheduled access in ieee 802 11ax wlans"):
  with members:
    - "potential and pitfalls of multi armed bandits for decentralized spatial reuse in wlans"
    - "the tmb path loss model for 5 ghz indoor wifi scenarios on the empirical relationship between rssi mcs and spatial streams"
    - "collaborative spatial reuse in wireless networks via selfish multi armed bandits"
    - "breaking the interference barrier in dense wireless networks with interference alignment"
    - "ieee 802 11ax high efficiency wlans"
    - "traffic differentiation in dense collision free wlans using csma eca"

Connected through hyperedge HE-3 (induced by source paper "advanced ieee 802 11ax tcp aware scheduling under unreliable channels"):
  with members:
    - "coupled ieee 802 11ac and tcp goodput improvement using aggregation and reverse direction"
    - "simultaneous transmit and receive operation in next generation ieee 802 11 wlans a mac protocol design approach"
    - "ieee 802 11ax high efficiency wlans"
    - "resource management for ofdma based next generation 802 11 wlans"
    - "future evolution of csma protocols for the ieee 802 11 standard"

Connected through hyperedge HE-4 (induced by source paper "optimal resource allocation in ieee 802 11ax uplink ofdma with scheduled access"):
  with members:
    - "target wake time scheduled access in ieee 802 11ax wlans"
    - "ieee 802 11ax high efficiency wlans"
    - "resource management for ofdma based next generation 802 11 wlans"

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)