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: "wireless data center networks advances challenges and opportunities"
  Abstract: Data center networks (DCNs) are essential infrastructures to embrace the era of highly diversified massive amount of data generated by emerging technological applications. In order to store and process such a data deluge, today's DCNs have to deploy enormous length of wires to interconnect a plethora of servers and switches. Unfortunately, wired DCNs with uniform and inflexible link capacities expose several drawbacks such as high cabling cost and complexity, low space utilization, and lack of bandwidth efficiency. Wireless DCNs (WDCNs) have emerged as a promising solution to reduce the time, effort, and cost spent on deploying and maintaining the wires. Thanks to its reconfigurability and flexibility, WDCNs can deliver higher throughputs by efficiently utilizing the bandwidth and mitigate the chronic DCN problems of oversubscription and hotspots. Moreover, wireless links enhance the fault-tolerance and energy efficiency by eliminating the need for error-prone power-hungry switches. Accordingly, this paper first compares virtues and drawbacks of millimeter wave (mmWave), terahertz (THz), and optical wireless communications as potential candidates. Thereafter, an in-depth discussion on advances and challenges in WDCNs is provided including physical and virtual topology design, quality of service (QoS) provisioning, flow classification, data grooming, and load balancing. Finally, exciting research opportunities are presented to promote the prospects of WDCNs.

Connected through hyperedge HE-1 (induced by source paper "coded federated computing in wireless networks with straggling devices and imperfect csi"):
  with members:
    - "wireless network intelligence at the edge"
    - "speeding up distributed machine learning using codes"
    - "federated learning strategies for improving communication efficiency"
    - "a storage computation communication tradeoff for distributed computing"
    - "improved latency communication trade off for map shuffle reduce systems with stragglers"
    - "on the optimal recovery threshold of coded matrix multiplication"
    - "wireless mapreduce distributed computing"
    - "wireless map reduce distributed computing with full duplex radios and imperfect csi"

Connected through hyperedge HE-2 (induced by source paper "lightfdg an integrated approach to flow detection and grooming in optical wireless dcns"):
  with members:
    - "design and provision of traffic grooming for optical wireless data center networks"
    - "heavy hitter detection entirely in the data plane"
    - "on the capacity of free space optical intensity channels"

Connected through hyperedge HE-3 (induced by source paper "design and provision of traffic grooming for optical wireless data center networks"):
  with members:
    - "heterogeneous cloud radio access networks a new perspective for enhancing spectral and energy efficiencies"
    - "on the capacity of free space optical intensity channels"

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)