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 "pair wise markov random fields applied to the design of low complexity mimo detectors"):
  Abstract: Pair-wise Markov random fields (MRF) are considered for application to the development of low complexity, iterative MIMO detection. Specifically, we consider two types of MRF, namely, the fully-connected and ring-type. For the edge potentials, we use the bivariate Gaussian function obtained by marginalizing the posterior joint probability density under the Gaussian assumption. Since the corresponding factor graphs are sparse, in the sense that the number of edges connected to a factor node (edge degree) is only 2, the computations are much easier than that of ML, which is similar to the belief propagation (BP), or sum-product, algorithm that is run over the fully connected factor graph. The BER performances for non-Gaussian input are evaluated via simulation, and the results show the validity of the proposed algorithms. We also customize the algorithm for Gaussian input to obtain the Gaussian BP that is run over the two MRF and proves its convergence in mean to the linear MMSE estimates. The result lies on the same line of those in [16] and [24], but with differences in its graphical model and the message passing rule. Since the MAP estimator for the Gaussian input is equivalent to the linear MMSE estimator, it shows the optimality, in mean, of the scheme for Gaussian input.

Includes members:
  Member "gaussian belief propagation based multiuser detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fixed symbol aided random access scheme for machine to machine communications")
  Member "belief propagation based multi user detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "capacity achieving mimo noma iterative lmmse detection")

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)