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: "non orthogonal multiplexing of ultra reliable and broadband services in fog radio architectures"
  Abstract: The fifth generation (5G) of cellular systems is introducing Ultra-Reliable Low-Latency Communications (URLLC) services alongside more conventional enhanced Mobile BroadBand (eMBB) traffic. Furthermore, the 5G cellular architecture is evolving from a base station-centric deployment to a fog-like set-up that accommodates a flexible functional split between cloud and edge. In this paper, a novel solution is proposed that enables the non-orthogonal coexistence of URLLC and eMBB services by processing URLLC traffic at the Edge Nodes (ENs), while eMBB communications are handled centrally at a cloud processor as in a Cloud-Radio Access Network (C-RAN) system. This solution guarantees the low-latency requirements of the URLLC service by means of edge processing, e.g., for vehicle-to-cellular use cases, as well as the high spectral efficiency for eMBB traffic via centralized baseband processing. Both uplink and downlink are analyzed by accounting for the heterogeneous performance requirements of eMBB and URLLC traffic and by considering practical aspects such as fading, lack of channel state information for URLLC transmitters, rate adaptation for eMBB transmitters, finite fronthaul capacity, and different coexistence strategies, such as puncturing.

Connected through hyperedge HE-1 (induced by source paper "information centric grant free access for iot fog networks edge vs cloud detection and learning"):
  with members:
    - "start making sense semantic plane filtering and control for post 5g connectivity"
    - "cooperative deep reinforcement learning for multiple group nb iot networks optimization"
    - "deep joint source channel coding for wireless image transmission"
    - "5g wireless network slicing for embb urllc and mmtc a communication theoretic view"
    - "towards massive connectivity support for scalable mmtc communications in 5g networks"
    - "fog aided wireless networks for content delivery fundamental latency tradeoffs"
    - "a brief introduction to machine learning for engineers"
    - "fog computing based radio access networks issues and challenges"

Connected through hyperedge HE-2 (induced by source paper "space diversity based grant free random access for critical and non critical iot services"):
  with members:
    - "multiple relay slotted aloha performance analysis and bounds"
    - "modern random access for beyond 5g systems a multiple relay aloha perspective"
    - "information centric grant free access for iot fog networks edge vs cloud detection and learning"
    - "5g wireless network slicing for embb urllc and mmtc a communication theoretic view"
    - "grant free radio access for short packet communications over 5g networks"
    - "grant free radio access iot networks scalability analysis in coexistence scenarios"
    - "two user erasure interference channels with local delayed csit"
    - "coded slotted aloha a graph based method for uncoordinated multiple access"

Connected through hyperedge HE-3 (induced by source paper "multiplexing gain region of sectorized cellular networks with mixed delay constraints"):
  with members:
    - "resource allocation for multi user downlink urllc ofdma systems"
    - "coexistence of urllc and embb services in the c ran uplink an information theoretic study"
    - "channels with cooperation links that may be absent"
    - "layered uplink transmission in clustered cellular networks"
    - "optimal dynamic resource allocation for multi antenna broadcasting with heterogeneous delay constrained traffic"

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)