human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "generating functional analysis for iterative cdma multiuser detectors"
  Abstract: We investigate the detection dynamics of a soft parallel interference canceller (soft-PIC), which includes a hard-PIC as a special case, for code-division multiple-access (CDMA) multiuser detection, applied to a randomly spread, fully synchronous base-band uncoded CDMA channel model with additive white Gaussian noise under perfect power control in the large-system limit. We analyze the detection dynamics of some iterative detectors, namely soft-PIC, the Onsager-reaction-cancelling parallel interference canceller (ORC-PIC) and the belief-propagation-based detector (BP-based detector), by the generating functional analysis (GFA). The GFA allows us to study the asymptotic behavior of the dynamics in the infinitely large system without assuming the independence of messages. We study the detection dynamics and the stationary estimates of an iterative algorithm. #R##N#We also show the decoupling principle in iterative multiuser detection algorithms in the large-system limit. For a generic iterative multiuser detection algorithm with binary input, it is shown that the multiuser channel is equivalent to a bank of independent single-user additive non-Gaussian channels, whose signal-to-noise ratio degrades due to both the multiple-access interference and the Onsager reaction, at each stage of the algorithm. If an algorithm cancels the Onsager reaction, the equivalent single-user channels coincide with an additive white Gaussian noise channel. We also discuss ORC-PIC and the BP-based detector.

Connected through hyperedge HE-1 (induced by source paper "dynamical functional theory for compressed sensing"):
  with members:
    - "self averaging expectation propagation"
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "s amp approximate message passing for general matrix ensembles"
    - "signal recovery using expectation consistent approximation for linear observations"
    - "support recovery with sparsely sampled free random matrices"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "message passing algorithms for compressed sensing"

Question: Please tell me which class the center node 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)