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 "unifying message passing algorithms under the framework of constrained bethe free energy minimization"):
  Abstract: Variational message passing (VMP), belief propagation (BP) and expectation propagation (EP) have found their wide applications in complex statistical signal processing problems. In addition to viewing them as a class of algorithms operating on graphical models, this paper unifies them under an optimization framework, namely, Bethe free energy minimization with differently and appropriately imposed constraints. This new perspective in terms of constraint manipulation can offer additional insights on the connection between different message passing algorithms and is valid for a generic statistical model. It also founds a theoretical framework to systematically derive message passing variants. Taking the sparse signal recovery (SSR) problem as an example, a low-complexity EP variant can be obtained by simple constraint reformulation, delivering better estimation performance with lower complexity than the standard EP algorithm. Furthermore, we can resort to the framework for the systematic derivation of hybrid message passing for complex inference tasks. Notably, a hybrid message passing algorithm is exemplarily derived for joint SSR and statistical model learning with near-optimal inference performance and scalable complexity.

Includes members:
  Member "an iterative receiver for ofdm with sparsity based parametric channel estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "expectation propagation line spectral estimation")
  Member "hybrid vector perturbation precoding the blessing of approximate message passing":
    also connected through other hyperedges:
      (none)
  Member "random access in c ran for user activity detection with limited capacity fronthaul":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a dimension reduction based joint activity detection and channel estimation algorithm for massive access")
  Member "a bp mf ep based iterative receiver for joint phase noise estimation equalization and decoding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "probabilistic receiver architecture combining bp mf and ep for multi signal detection")
  Member "gaussian message passing iterative detection for mimo noma systems with massive access":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generalized approximate message passing for massive mimo mmwave channel estimation with laplacian prior")
  Member "message passing receiver for joint channel estimation and decoding in 3d massive mimo ofdm systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "probabilistic receiver architecture combining bp mf and ep for multi signal detection")
  Member "optimality of large mimo detection via approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "s amp approximate message passing for general matrix ensembles":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "inference in multi layer networks with matrix valued unknowns")

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)