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 "analysis of massive mimo enabled downlink wireless backhauling for full duplex small cells"):
  Abstract: Recent advancements in self-interference (SI) cancellation capability of low-power wireless devices motivate in-band full-duplex (FD) wireless backhauling in small cell networks (SCNs). In-band FD wireless backhauling concurrently allows the use of the same frequency spectrum for the backhaul as well as access links of the small cells. In this paper, using tools from stochastic geometry, we develop a framework to model the downlink rate coverage probability of a user in a given SCN with massive multiple-input–multiple-output (MIMO)-enabled wireless backhauls. The considered SCN is composed of a mixture of small cells that are configured in either in-band or out-of-band backhaul modes with a certain probability. The performance of the user in the considered hierarchical network is limited by several sources of interference, such as the backhaul interference, small cell base station (SBS)-to-SBS interference, and the SI. Moreover, due to the channel hardening effect in massive MIMO, the backhaul links only experience long term channel effects, whereas the access links experience both the long term and the short term channel effects. Consequently, the developed framework is flexible to characterize different sources of interference while capturing the heterogeneity of the access and backhaul channels. In specific scenarios, the framework enables deriving closed-form coverage probability expressions. Under perfect backhaul coverage, the simplified expressions are utilized to optimize the proportion of in-band and out-of-band small cells in the SCN in the closed form. Finally, a few remedial solutions are proposed that can potentially mitigate the backhaul interference and in turn improve the performance of in-band FD wireless backhauling. Numerical results investigate the scenarios in which in-band wireless backhauling is useful and demonstrate that maintaining a correct proportion of in-band and out-of-band FD small cells is crucial in wireless backhauled SCNs.

Includes members:
  Member "joint downlink cell association and bandwidth allocation for wireless backhauling in two tier hetnets with large scale antenna arrays":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "backhaul aware optimization of uav base station location and bandwidth allocation for profit maximization")
  Member "heterogeneous cellular networks using wireless backhaul fast admission control and large system analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secrecy outage and diversity analysis of multiple cooperative source destination pairs")
  Member "small cell in band wireless backhaul in massive mimo systems a cooperation of next generation techniques":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "integrated access and backhaul optimization for millimeter wave heterogeneous networks")
  Member "5g wireless backhaul networks challenges and research advances":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reinforcement learning based cooperative coded caching under dynamic popularities in ultra dense networks")
  Member "full duplex cloud radio access networks an information theoretic viewpoint":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "performance of network assisted full duplex for cell free massive mimo")
  Member "optimal user cell association for massive mimo wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reinforcement learning based vehicle cell association algorithm for highly mobile millimeter wave communication")
  Member "hybrid full half duplex system analysis in heterogeneous wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "edge computing enabled cell free massive mimo systems")
  Member "cognitive and energy harvesting based d2d communication in cellular networks stochastic geometry modeling and analysis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "analysis of d2d communication with rf energy harvesting and interference management")

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.IT(Information Theory)