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: "performance of time delay estimation in a cognitive radar"
  Abstract: A cognitive radar adapts the transmit waveform in response to changes in the radar and target environment. In this work, we analyze the recently proposed sub-Nyquist cognitive radar wherein the total transmit power in a multi-band cognitive waveform remains the same as its full-band conventional counterpart. For such a system, we derive lower bounds on the mean-squared-error (MSE) of a single-target time delay estimate. We formulate a procedure to select the optimal bands, and recommend distribution of the total power in different bands to enhance the accuracy of delay estimation. In particular, using Cramer-Rao bounds, we show that equi-width subbands in cognitive radar always have better delay estimation than the conventional radar. Further analysis using Ziv-Zakai bound reveals that cognitive radar performs well in low signal-to-noise (SNR) regions.

Connected through hyperedge HE-1 (induced by source paper "generalized bayesian cramer rao inequality via information geometry of relative alpha entropy"):
  with members:
    - "cramer rao lower bounds arising from generalized csiszar divergences"
    - "information geometric approach to bayesian lower error bounds"
    - "minimization problems based on relative alpha entropy ii reverse projection"
    - "minimization problems based on relative alpha entropy i forward projection"
    - "guessing under source uncertainty"

Connected through hyperedge HE-2 (induced by source paper "dictionary learning for adaptive gpr landmine classification"):
  with members:
    - "sub nyquist radar principles and prototypes"
    - "on learning with shift invariant structures"
    - "convolutional dictionary learning a comparative review and new algorithms"
    - "fast low rank shared dictionary learning for image classification"
    - "online dictionary learning aided target recognition in cognitive gpr"
    - "trainlets dictionary learning in high dimensions"
    - "cognitive sub nyquist hardware prototype of a collocated mimo radar"
    - "fast r cnn"

Connected through hyperedge HE-3 (induced by source paper "information geometric approach to bayesian lower error bounds"):
  with members:
    - "cramer rao type bounds for sparse bayesian learning"
    - "cramer rao lower bound and information geometry"

Connected through hyperedge HE-4 (induced by source paper "online dictionary learning aided target recognition in cognitive gpr"):
  with members:
    - "cognitive sub nyquist hardware prototype of a collocated mimo radar"

Connected through hyperedge HE-5 (induced by source paper "spectrum sharing radar coexistence via xampling"):
  with members:
    - "analog to digital cognitive radio sampling detection and hardware"
    - "sub nyquist cyclostationary detection for cognitive radio"
    - "sub nyquist channel estimation over ieee 802 11ad link"
    - "on the co existence of td lte and radar over 3 5 ghz band an experimental study"
    - "spectrum sharing between a surveillance radar and secondary wi fi networks"
    - "recursive recovery of sparse signal sequences from compressive measurements a review"
    - "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach"
    - "pattern coupled sparse bayesian learning for recovery of block sparse signals"

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)