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 "joint transmit and receive filter optimization for sub nyquist wireless channel estimation"):
  Abstract: In this article a framework is presented for the joint optimization of the analog transmit and receive filter with respect to a channel estimation problem. At the receiver, conventional signal processing systems restrict the bandwidth of the analog pre-filter $B$ to the rate of the analog-to-digital converter $f_s$ in order to comply with the well-known Nyquist sampling theorem. In contrast, here we consider a transceiver that by design violates the common paradigm $B\leq f_s$. To this end, at the receiver we allow for a higher pre-filter bandwidth $B>f_s$ and study the achievable channel estimation accuracy under a fixed sampling rate when the transmit and receive filter are jointly optimized with respect to the Bayesian Cram\'{e}r-Rao lower bound. For the case of a channel with unknown delay-Doppler shift we show how to approximate the required Fisher information matrix and solve the transceiver design problem by an alternating optimization algorithm. The presented approach allows us to explore the Pareto-optimal region spanned by transmit and receive filters which are favorable under a weighted mean squared error criterion. We discuss the complexity of the obtained transceiver design by visualizing the resulting ambiguity function. Finally, we verify the achievable performance of the proposed designs by Monte-Carlo simulations of a likelihood-based channel estimator.

Includes members:
  Member "performance bounds and design criteria for estimating finite rate of innovation signals":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint transmit and receive filter optimization for sub nyquist delay doppler estimation")
  Member "xampling at the rate of innovation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint transmit and receive filter optimization for sub nyquist delay doppler estimation")
  Member "xampling signal acquisition and processing in union of subspaces":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fast compressed power spectrum estimation towards a practical solution for wideband spectrum sensing")

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)