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 "joint channel estimation and data detection for multihop ofdm relaying system under unknown channel orders and doppler frequencies"):
  Abstract: In this paper, channel estimation and data detection for multihop relaying orthogonal frequency division multiplexing (OFDM) system is investigated under time-varying channel. Different from previous works, which highly depend on the statistical information of the doubly-selective channel (DSC) and noise to deliver accurate channel estimation and data detection results, we focus on more practical scenarios with unknown channel orders and Doppler frequencies. Firstly, we integrate the multilink, multihop channel matrices into one composite channel matrix. Then, we formulate the unknown channel using generalized complex exponential basis expansion model (GCE-BEM) with a large oversampling factor to introduce channel sparsity on delay-Doppler domain. To enable the identification of nonzero entries, sparsity enhancing Gaussian distributions with Gamma hyperpriors are adopted. An iterative algorithm is developed under variational inference (VI) framework. The proposed algorithm iteratively estimate the channel, recover the unknown data using Viterbi algorithm and learn the channel and noise statistical information, using only limited number of pilot subcarrier in one OFDM symbol. Simulation results show that, without any statistical information, the performance of the proposed algorithm is very close to that of the optimal channel estimation and data detection algorithm, which requires specific information on system structure, channel tap positions, channel lengths, Doppler shifts as well as noise powers.

Includes members:
  Member "variational relevance vector machines":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "probabilistic feature selection and classification vector machine")
  Member "compressive estimation of doubly selective channels in multicarrier systems leakage effects and sparsity enhancing processing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "eliminating impulsive noise in pilot aided ofdm channels via dual of penalized atomic norm")

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)