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 "inter operator resource management for millimeter wave multi hop backhaul networks"):
  Abstract: In this paper, a novel framework is proposed for optimizing the operation and performance of a large-scale, multi-hop millimeter wave (mmW) backhaul within a wireless small cell network (SCN) that encompasses multiple mobile network operators (MNOs). The proposed framework enables the small base stations (SBSs) to jointly decide on forming the multi-hop, mmW links over backhaul infrastructure that belongs to multiple, independent MNOs, while properly allocating resources across those links. In this regard, the problem is addressed using a novel framework based on matching theory that is composed to two, highly inter-related stages: a multi-hop network formation stage and a resource management stage. One unique feature of this framework is that it jointly accounts for both wireless channel characteristics and economic factors during both network formation and resource management. The multi-hop network formation stage is formulated as a one-to-many matching game which is solved using a novel algorithm, that builds on the so-called deferred acceptance algorithm and is shown to yield a stable and Pareto optimal multi-hop mmW backhaul network. Then, a one-to-many matching game is formulated to enable proper resource allocation across the formed multi-hop network. This game is then shown to exhibit peer effects and, as such, a novel algorithm is developed to find a stable and optimal resource management solution that can properly cope with these peer effects. Simulation results show that the proposed framework yields substantial gains, in terms of the average sum rate, reaching up to 27% and 54%, respectively, compared to a non-cooperative scheme in which inter-operator sharing is not allowed and a random allocation approach. The results also show that our framework provides insights on how to manage pricing and the cost of the cooperative mmW backhaul network for the MNOs.

Includes members:
  Member "user association in 5g networks a survey and an outlook":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a tutorial on beam management for 3gpp nr at mmwave frequencies")
  Member "5g ultra dense cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reinforcement learning based cooperative coded caching under dynamic popularities in ultra dense networks")
  Member "joint deployment of small cells and wireless backhaul links in next generation networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cooperative wireless backhauling")
  Member "probabilistic omnidirectional path loss models for millimeter wave outdoor communications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "early acceptance matching game for user association in 5g cellular hetnet")
  Member "matching theory for backhaul management in small cell networks with mmwave capabilities":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "joint path selection and rate allocation framework for 5g self backhauled mm wave networks")
  Member "tractable model for rate in self backhauled millimeter wave cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "joint path selection and rate allocation framework for 5g self backhauled mm wave networks")
  Member "distributed resource allocation in device to device enhanced cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "when distributed outperforms centralized scheduling in d2d enabled cellular networks")
  Member "matching theory for future wireless networks fundamentals and applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi agent meta reinforcement learning for self powered and sustainable edge computing systems")

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.GT(Computer Science and Game Theory)