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 "inference from randomized transmissions by many backscatter sensors"):
  Abstract: Attaining the vision of Smart Cities requires the deployment of an enormous number of sensors for monitoring various conditions of the environment. Backscatter-sensors have emerged to be a promising solution due to the uninterruptible energy supply and relative simple hardwares. On the other hand, backscatter-sensors with limited signal-processing capabilities are unable to support conventional algorithms for multiple-access and channel-training. Thus, the key challenge in designing backscatter-sensor networks is to enable readers to accurately detect sensing-values given simple ALOHA random access, primitive transmission schemes, and no knowledge of channel-states. We tackle this challenge by proposing the novel framework of backscatter sensing featuring random-encoding at sensors and statistical-inference at readers. Specifically, assuming the on/off keying for backscatter transmissions, the practical random-encoding scheme causes the on/off transmission of a sensor to follow a distribution parameterized by the sensing values. Facilitated by the scheme, statistical-inference algorithms are designed to enable a reader to infer sensing-values from randomized transmissions by multiple sensors. The specific design procedure involves the construction of Bayesian networks, namely deriving conditional distributions for relating unknown parameters and variables to signals observed by the reader. Then based on the Bayesian networks and the well-known expectation-maximization principle, inference algorithms are derived to recover sensing-values.

Includes members:
  Member "wirelessly powered backscatter communications waveform design and snr energy tradeoff":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "energy efficiency of distributed antenna systems with wireless power transfer")
  Member "detection algorithms for communication systems using deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")
  Member "ambient backscatter communications a contemporary survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "irs based wireless jamming attacks when jammers can attack without power")
  Member "physical layer security in mimo backscatter wireless systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "wireless powered asynchronous backscatter networks with sporadic short packets performance analysis and optimization")
  Member "full duplex backscatter interference networks based on time hopping spread spectrum":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "backscatter data collection with unmanned ground vehicle mobility management and power allocation")
  Member "wirelessly powered backscatter communication networks modeling coverage and capacity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "clustered millimeter wave networks with non orthogonal multiple access")
  Member "multi antenna wireless energy transfer for backscatter communication systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "energy efficient uav backscatter communication with joint trajectory design and resource optimization")

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)