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 "joint path selection and rate allocation framework for 5g self backhauled mm wave networks"):
  Abstract: Owing to severe path loss and unreliable transmission over a long distance at higher frequency bands, this paper investigates the problem of path selection and rate allocation for multi-hop self-backhaul millimeter-wave (mm-wave) networks. Enabling multi-hop mm-wave transmissions raises a potential issue of increased latency, and thus, this paper aims at addressing the fundamental questions:  how to select the best multi-hop paths and how to allocate rates over these paths subject to latency constraints?  In this regard, a new system design, which exploits multiple antenna diversity, mm-wave bandwidth, and traffic splitting techniques, is proposed to improve the downlink transmission. The studied problem is cast to as a network utility maximization, subject to the upper delay bound constraint, network stability, and network dynamics. By leveraging stochastic optimization, the problem is decoupled into: 1) path selection and 2) rate allocation sub-problems, whereby a framework which selects the best paths is proposed using reinforcement learning techniques. Moreover, the rate allocation is a non-convex program, which is converted into a convex one by using the successive convex approximation method. Via mathematical analysis, the comprehensive performance analysis and convergence proof are provided for the proposed solution. The numerical results show that the proposed approach ensures reliable communication with a guaranteed probability of up to 99.9999% and reduces latency by 50.64% and 92.9% as compared to baseline models. Furthermore, the results showcase the key tradeoff between latency and network arrival rate.

Includes members:
  Member "path selection and rate allocation in self backhauled mmwave networks":
    also connected through other hyperedges:
      (none)
  Member "ultra reliable communication in 5g mmwave networks a risk sensitive approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "wireless edge computing with latency and reliability guarantees")
  Member "energy efficient interactive beam alignment for millimeter wave networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mobility and blockage aware communications in millimeter wave vehicular networks")
  Member "ultra reliable and low latency wireless communication tail risk and scale":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimized age of information tail for ultra reliable low latency communications in vehicular networks")
  Member "toward low latency and ultra reliable virtual reality":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "wireless edge computing with latency and reliability guarantees")
  Member "millimeter wave v2v communications distributed association and beam alignment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "wireless edge computing with latency and reliability guarantees")
  Member "capacity scaling of cellular networks impact of bandwidth infrastructure density and number of antennas":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "millimeter wave integrated access and backhaul in 5g performance analysis and design insights")
  Member "joint load balancing and interference mitigation in 5g heterogeneous networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ultra reliable and low latency wireless communication tail risk and scale")

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)