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: "optimal joint target detection and parameter estimation by mimo radar"
  Abstract: We consider multiple-input multiple-output (MIMO) radar systems with widely spaced antennas. Such antenna configuration facilitates capturing the inherent diversity gain due to independent signal dispersion by the target scatterers. We consider a new MIMO radar framework for detecting a target that lies in an unknown location. This is in contrast with conventional MIMO radars which break the space into small cells and aim at detecting the presence of a target in a specified cell. We treat this problem through offering a novel composite hypothesis testing framework for target detection when 1) one or more parameters of the target are unknown and we are interested in estimating them, and 2) only a finite number of observations are available. The test offered optimizes a metric which accounts for both detection and estimation accuracies. In this paper, as the parameter of interest we focus on the vector of time-delays that the waveforms undergo from being emitted by the transmit antennas until being observed by the receive antennas. The analytical and empirical results establish that for the proposed joint target detection and time-delay estimation framework, MIMO radars exhibit significant gains over phased-array radars for extended targets which consist of multiple independent scatterers. For point targets modeled as single scatterers, however, the detection/estimation accuracies of MIMO and phased-array radars for this specific setup (joint target detection and time-delay estimation) are comparable.

Connected through hyperedge HE-1 (induced by source paper "recursive distributed detection for composite hypothesis testing nonlinear observation models in additive gaussian noise"):
  with members:
    - "distributed detection over adaptive networks refined asymptotics and the role of connectivity"
    - "diffusion based adaptive distributed detection steady state performance in the slow adaptation regime"
    - "distributed inference over directed networks performance limits and optimal design"
    - "distributed bayesian quickest change detection in sensor networks via two layer large deviation analysis"
    - "multitask diffusion adaptation over networks"
    - "distributed detection over noisy networks large deviations analysis"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-2 (induced by source paper "recursive distributed detection for composite hypothesis testing algorithms and asymptotics"):
  with members:
    - "distributed bayesian quickest change detection in sensor networks via two layer large deviation analysis"
    - "multitask diffusion adaptation over networks"
    - "distributed detection over noisy networks large deviations analysis"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-3 (induced by source paper "performance analysis of target parameters estimation using multiple widely separated antenna arrays"):
  with members:
    - "target localization accuracy gain in mimo radar based systems"
    - "mimo radar using compressive sampling"

Connected through hyperedge HE-4 (induced by source paper "ambiguity function of the transmit beamspace based mimo radar"):
  with members:
    - "spatial compressive sensing for mimo radar"
    - "efficient transmit beamspace design for search free based doa estimation in mimo radar"
    - "moving target parameters estimation in noncoherent mimo radar systems"
    - "transmit energy focusing for doa estimation in mimo radar with colocated antennas"
    - "mimo radar using compressive sampling"
    - "phased mimo radar a tradeoff between phased array and mimo radars"
    - "diversity integration tradeoffs in mimo detection"

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)