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 "sum throughput maximization in multi tag backscattering to multiantenna reader"):
  Abstract: Backscatter communication (BSC) is being realized as the core technology for pervasive sustainable Internet-of-Things applications. However, owing to the resource-limitations of passive tags, the efficient usage of multiple antennas at the reader is essential for both downlink excitation and uplink detection. This work targets at maximizing the achievable sum-backscattered-throughput by jointly optimizing the transceiver (TRX) design at the reader and backscattering coefficients (BC) at the tags. Since, this joint problem is nonconvex, we first present individually-optimal designs for the TRX and BC. We show that with precoder and {combiner} designs at the reader respectively targeting downlink energy beamforming and uplink Wiener filtering operations, the BC optimization at tags can be reduced to a binary power control problem. Next, the asymptotically-optimal joint-TRX-BC designs are proposed for both low and high signal-to-noise-ratio regimes. Based on these developments, an iterative low-complexity algorithm is proposed to yield an efficient jointly-suboptimal design. Thereafter, we discuss the practical utility of the proposed designs to other application settings like wireless powered communication networks and BSC with imperfect channel state information. Lastly, selected numerical results, validating the analysis and shedding novel insights, demonstrate that the proposed designs can yield significant enhancement in the sum-backscattered throughput over existing benchmarks.

Includes members:
  Member "optimal channel estimation for reciprocity based backscattering with a full duplex mimo reader":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi tag backscattering to mimo reader channel estimation and throughput fairness")
  Member "cooperative ambient backscatter communications for green internet of things":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "capitalizing backscatter aided hybrid relay communications with wireless energy harvesting")
  Member "fractional programming for communication systems part i power control and beamforming":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "energy efficiency optimization for downlink massive mimo with statistical csit")
  Member "retrodirective large antenna energy beamforming in backscatter multi user networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a retrodirective wireless power transfer scheme for ambient backscatter systems")
  Member "transmit precoding and receive power splitting for harvested power maximization in mimo swipt systems":
    also connected through other hyperedges:
      (none)
  Member "max min sinr in large scale single cell mu mimo asymptotic analysis and low complexity transceivers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "asymptotic max min sinr analysis of reconfigurable intelligent surface assisted miso systems")
  Member "inference from randomized transmissions by many backscatter sensors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "over the air computation systems optimization analysis and scaling laws")
  Member "throughput optimization for massive mimo systems powered by wireless energy transfer":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "wirelessly powered sensor networks power allocation for channel estimation and energy beamforming")

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)