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 "multiple d2d multicasts in underlay cellular networks"):
  Abstract: Multicasting for disseminating popular data is an interesting solution for improving the energy and spectral efficiencies of cellular networks. To improve the achievable performance of such networks, underlay device-to-device (D2D) multicast communication offers a practical solution. However, despite significant potential for providing higher throughput and lower delay, implementing underlay D2D multicast communication poses several challenges, such as mutual interference among cellular users (CUs) and D2D multicast groups (MGs), and overhead signaling to provide channel state information, that may limit potential gains. We study a scenario where multiple D2D multicast groups may share a CU's uplink channel. We formulate an optimization problem to maximize the achievable system throughput while fulfilling quality of service (QoS) requirements of every CU and D2D MGs, subject to their corresponding maximum transmit power constraints. The formulated optimization problem is an instance of mixed integer non-linear programming (MINLP) problem, which is computationally intractable, in general. Therefore, to find a feasible solution, we propose a pragmatic two-step process of channel allocation and power allocation. In the first-step, we propose a channel allocation algorithm, which determines the subset of MGs that may share a channel subject to criteria based on two different parameters: interference and outage probabilities. Then, we propose an algorithm to allocate power to these MG subsets that maximizes the system throughput, while satisfying transmit power constraint. Numerical results show the efficacy of proposed approach in terms of higher achievable sum throughput and better spectrum efficiency with respect to various existing schemes.

Includes members:
  Member "d2d multicast in underlay cellular networks with exclusion zones":
    also connected through other hyperedges:
      (none)
  Member "optimal resource allocation in multicast device to device communications underlaying lte networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "energy efficient power and channel allocation in underlay device to multi device communications")
  Member "resource allocation under channel uncertainties for relay aided device to device communication underlaying lte a cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "relay assisted device to device communication approaches and issues")
  Member "modeling analysis and optimization of multicast device to device transmissions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "d2d multicast in underlay cellular networks with exclusion zones")
  Member "efficiency resource allocation for device to device underlay communication systems a reverse iterative combinatorial auction based approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sub channel allocation for device to device underlaying full duplex mmwave small cells using coalition formation games")

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)