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: "optimal channel estimation for reciprocity based backscattering with a full duplex mimo reader"
  Abstract: Backscatter communication (BSC) technology can enable ubiquitous deployment of low-cost sustainable wireless devices. In this paper, we investigate the efficacy of a full-duplex multiple-input-multiple-output reader for enhancing the limited communication range of monostatic BSC systems. As this performance is strongly influenced by the channel estimation (CE) quality, we first derive a novel least-squares estimator for the forward and backward links between the reader and the tag, assuming that reciprocity holds and   $K$   orthogonal pilots are transmitted from the first   $K$   antennas of an   $N$   antenna reader. We also obtain the corresponding linear minimum-mean square-error estimate for the backscattered channel. After defining the transceiver design at the reader using these estimates, we jointly optimize the number of orthogonal pilots and energy allocation for the CE and information decoding phases to maximize the average backscattered signal-to-noise ratio (SNR) for efficiently decoding the tag's messages. The unimodality of this SNR in optimization variables along with a tight analytical approximation for the jointly global optimal design is also discoursed. Lastly, the selected numerical results validate the proposed analysis, present key insights into the optimal resource utilization at reader, and quantify the achievable gains over the benchmark schemes.

Connected through hyperedge HE-1 (induced by source paper "sum throughput maximization in multi tag backscattering to multiantenna reader"):
  with members:
    - "cooperative ambient backscatter communications for green internet of things"
    - "fractional programming for communication systems part i power control and beamforming"
    - "retrodirective large antenna energy beamforming in backscatter multi user networks"
    - "transmit precoding and receive power splitting for harvested power maximization in mimo swipt systems"
    - "max min sinr in large scale single cell mu mimo asymptotic analysis and low complexity transceivers"
    - "inference from randomized transmissions by many backscatter sensors"
    - "throughput optimization for massive mimo systems powered by wireless energy transfer"
    - "multi antenna wireless energy transfer for backscatter communication systems"

Connected through hyperedge HE-2 (induced by source paper "multi tag backscattering to mimo reader channel estimation and throughput fairness"):
  with members:
    - "sum throughput maximization in multi tag backscattering to multiantenna 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)