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 "resource allocation and power control in cooperative small cell networks in frequency selective channels with backhaul constraint"):
  Abstract: A joint resource allocation (RA), user association (UA), and power control (PC) problem is addressed for proportional fairness maximization in a cooperative multiuser downlink small cell network with limited backhaul capacity, based on orthogonal frequency division multiplexing. Previous studies have relaxed the per-resource-block (RB) RA and UA problem to a continuous optimisation problem based on long-term signal-to-noise-ratio, because the original problem is known as a combinatorial NP-hard problem. We tackle the original per-RB RA and UA problem to obtain a near-optimal solution with feasible complexity. We show that the conventional dual problem approach for RA cannot find the solution satisfying the conventional KKT conditions. Inspired by the dual problem approach, however, we derive the first order optimality conditions for the considered RA, UA, and PC problem, and propose a sequential optimization method for finding the solution. The overall proposed scheme can be implemented with feasible complexity even with a large number of system parameters. Numerical results show that the proposed scheme achieves the proportional fairness close to its outer bound with unlimited backhaul capacity in the low backhaul capacity regime and to that of a carefully-designed genetic algorithm with excessive generations but without backhaul constraint in the high backhaul capacity regime.

Includes members:
  Member "backhaul aware user association and resource allocation for energy constrained hetnets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning oriented cross entropy approach to user association in load balanced hetnet")
  Member "tier aware resource allocation in ofdma macrocell small cell networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed optimization of hierarchical small cell networks a gnep framework")
  Member "sinr and throughput scaling in ultradense urban cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sum rate maximization of multicell miso networks with limited information exchange")
  Member "distributed pricing based user association for downlink heterogeneous cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reinforcement learning based vehicle cell association algorithm for highly mobile millimeter wave communication")
  Member "optimal user cell association for massive mimo wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reinforcement learning based vehicle cell association algorithm for highly mobile millimeter wave communication")
  Member "user association for load balancing in heterogeneous cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reinforcement learning based cooperative coded caching under dynamic popularities in ultra dense networks")
  Member "resource allocation for downlink cellular ofdma systems part ii practical algorithms and optimal reuse factor":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "nearly optimal resource allocation for downlink ofdma in 2 d cellular networks")
  Member "resource allocation for downlink cellular ofdma systems part i optimal allocation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "spectral and energy efficiency of parallel gaussian broadcast channels")

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)