human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "dynamic multi connectivity activation for ultra reliable and low latency communication"):
  Abstract: Multi-connectivity (MC) with packet duplication, where the same data packet is duplicated and transmitted from multiple transmitters, is proposed in 5G New Radio as a reliability enhancement feature. However, it is found to be resource inefficient, since radio resources from more than one transmitters are required to serve a single user. Improving the performance enhancement vs. resource utilization tradeoff with MC is therefore a key design challenge. This work proposes a heuristic resource efficient latency-aware dynamic MC algorithm which activates MC selectively such that its benefits are harnessed for critical users, while minimizing the corresponding resource usage. Numerical results indicate that the proposed algorithm can deliver the outage performance gains of legacy MC schemes while requiring up to 45% less resources.

Includes members:
  Member "on the resource utilization of multi connectivity transmission for urllc services in 5g new radio":
    also connected through other hyperedges:
      (none)
  Member "ultrareliable and low latency communication techniques for tactile internet services":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust urllc packet scheduling of ofdm systems")
  Member "resource allocation and harq optimization for urllc traffic in 5g wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust urllc packet scheduling of ofdm systems")
  Member "risk aware resource allocation for urllc challenges and strategies with machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "experienced deep reinforcement learning with generative adversarial networks gans for model free ultra reliable low latency communication")

Question: Please tell me which class the source paper 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)