human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "energy efficient uav backscatter communication with joint trajectory design and resource optimization"):
  with members:
    - "large intelligent surface antennas lisa making reflective radios smart"
    - "uav data collection over noma backscatter networks uav altitude and trajectory optimization"
    - "cooperative ambient backscatter communications for green internet of things"
    - "symbiotic radio a new communication paradigm for passive internet of things"
    - "energy minimization for wireless communication with rotary wing uav"
    - "spectrum sharing for internet of things a survey"
    - "throughput maximization for uav enabled wireless powered communication networks"
    - "optimal resource allocation in full duplex ambient backscatter communication networks for wireless powered iot"

Connected through hyperedge HE-2 (induced by source paper "multi tag backscattering to mimo reader channel estimation and throughput fairness"):
  with members:
    - "optimal channel estimation for reciprocity based backscattering with a full duplex mimo reader"
    - "multiuser wirelessly powered backscatter communications nonlinearity waveform design and sinr energy tradeoff"
    - "retrodirective large antenna energy beamforming in backscatter multi user networks"
    - "inference from randomized transmissions by many backscatter sensors"
    - "backscatter multiplicative multiple access systems fundamental limits and practical design"
    - "ambient backscatter communications a contemporary survey"
    - "throughput optimization for massive mimo systems powered by wireless energy transfer"
    - "multi antenna wireless energy transfer for backscatter communication systems"

Question: Please tell me which class the center node 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)