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 correlated patterns a unified theory for perceptron learning and linear vector channels"):
  Abstract: A framework to analyze inference performance in densely connected single-layer feed-forward networks is developed for situations where a given data set is composed of correlated patterns. The framework is based on the assumption that the left and right singular value bases of the given pattern matrix are generated independently and uniformly from Haar measures. This assumption makes it possible to characterize the objective system by a single function of two variables which is determined by the eigenvalue spectrum of the cross-correlation matrix of the pattern matrix. Links to existing methods for analysis of perceptron learning and Gaussian linear vector channels and an application to a simple but nontrivial problem are also shown.

Includes members:
  Member "asymptotic analysis of general multiuser detectors in mimo ds cdma channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "asymptotic performance analysis of a k hop amplify and forward relay mimo channel")
  Member "vector precoding for wireless mimo systems and its replica analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "expectation propagation for approximate inference free probability framework")
  Member "analysis of belief propagation for non linear problems the example of cdma or how to prove tanaka s formula":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "understanding phase transitions via mutual information and mmse")
  Member "analysis of cdma systems that are characterized by eigenvalue spectrum":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spectral method for phase retrieval an expectation propagation perspective")
  Member "various solutions to the firing squad synchronization problems":
    also connected through other hyperedges:
      (none)
  Member "improved message passing for inference in densely connected systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "capacity optimality of amp in coded systems")
  Member "belief propagation based multi user detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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)