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 "probabilistic receiver architecture combining bp mf and ep for multi signal detection"):
  Abstract: Receiver algorithms which combine belief propagation (BP) with the mean field (MF) approximation are well-suited for inference of both continuous and discrete random variables. In wireless scenarios involving detection of multiple signals, the standard construction of the combined BP-MF framework includes the equalization or multi-user detection functions within the MF subgraph. In this paper, we show that the MF approximation is not particularly effective for multi-signal detection. We develop a new factor graph construction for application of the BP-MF framework to problems involving the detection of multiple signals. We then develop a low-complexity variant to the proposed construction in which Gaussian BP is applied to the equalization factors. In this case, the factor graph of the joint probability distribution is divided into three subgraphs: (i) a MF subgraph comprised of the observation factors and channel estimation, (ii) a Gaussian BP subgraph which is applied to multi-signal detection, and (iii) a discrete BP subgraph which is applied to demodulation and decoding. Expectation propagation is used to approximate discrete distributions with a Gaussian distribution and links the discrete BP and Gaussian BP subgraphs. The result is a probabilistic receiver architecture with strong theoretical justification which can be applied to multi-signal detection.

Includes members:
  Member "low complexity sparse bayesian learning using combined belief propagation and mean field with a stretched factor graph":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "iterative channel estimation using lse and sparse message passing for mmwave mimo systems")
  Member "a low complexity receiver using combined bp mf for joint channel estimation and decoding in ofdm systems":
    also connected through other hyperedges:
      (none)
  Member "a low complexity ofdm receiver with combined gamp and mf message passing":
    also connected through other hyperedges:
      (none)
  Member "a bp mf ep based iterative receiver for joint phase noise estimation equalization and decoding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unifying message passing algorithms under the framework of constrained bethe free energy minimization")
  Member "approximate joint map detection of co channel signals":
    also connected through other hyperedges:
      (none)
  Member "message passing receiver for joint channel estimation and decoding in 3d massive mimo ofdm systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unifying message passing algorithms under the framework of constrained bethe free energy minimization")
  Member "joint sparse channel estimation and decoding continuous and discrete domain sparsity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "low complexity sparse bayesian learning using combined belief propagation and mean field with a stretched factor graph")
  Member "expectation propagation for approximate bayesian inference":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bayesian deep learning and a probabilistic perspective of generalization")

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)