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: "joint multiple symbol differential detection and channel decoding for noncoherent uwb impulse radio by belief propagation"
  Abstract: This paper proposes a belief propagation (BP) message passing algorithm based joint multiple symbol differential detection (MSDD) and channel decoding scheme for noncoherent differential ultra-wideband impulse radio (UWB-IR) systems. MSDD is an effective means to improve the performance of noncoherent differential UWB-IR systems. To optimize the overall detection and decoding performance, in this paper, we propose a novel soft-in soft-out (SISO) MSDD scheme and its integration with SISO channel decoding for noncoherent differential UWB-IR. we first propose a new auto-correlation receiver (AcR) architecture to sample the received UWB-IR signal. The proposed AcR can exploit the dependencies (imposed by the differential modulation) among data symbols throughout the whole packet. The signal probabilistic model has a hidden Markov chain structure. We use a factor graph to represent this hidden Markov chain. Then, we apply BP message passing algorithm on the factor graph to develop a SISO MSDD scheme, which has better performance than the previous MSDD scheme and is easy to be integrated with SISO channel decoding to form a joint MSDD and channel decoding scheme. Simulation results indicate the performance advantages of our MSDD scheme and joint MSDD and channel decoding scheme.

Connected through hyperedge HE-1 (induced by source paper "a belief propagation based framework for soft multiple symbol differential detection"):
  with members:
    - "fifty years of mimo detection the road to large scale mimos"
    - "approximate bayesian probabilistic data association aided iterative detection for mimo systems using arbitrary m ary modulation"
    - "ber analysis of decision feedback multiple symbol detection in noncoherent mimo ultra wideband systems"
    - "algebraic distributed differential space time codes with low decoding complexity"

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)