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 "variable earns profit improved adaptive channel estimation using sparse vss nlms algorithms"):
  Abstract: Accurate channel estimation is essential for broadband wireless communications. As wireless channels often exhibit sparse structure, the adaptive sparse channel estimation algorithms based on normalized least mean square (NLMS) have been proposed, e.g., the zero-attracting NLMS (ZA-NLMS) algorithm and reweighted zero-attracting NLMS (RZA-NLMS). In these NLMS-based algorithms, the step size used to iteratively update the channel estimate is a critical parameter to control the estimation accuracy and the convergence speed (so the computational cost). However, invariable step-size (ISS) is usually used in conventional algorithms, which leads to provide performance loss or/and low convergence speed as well as high computational cost. To solve these problems, based on the observation that large step size is preferred for fast convergence while small step size is preferred for accurate estimation, we propose to replace the ISS by variable step size (VSS) in conventional NLMS-based algorithms to improve the adaptive sparse channel estimation in terms of bit error rate (BER) and mean square error (MSE) metrics. The proposed VSS-ZA-NLMS and VSS-RZA-NLMS algorithms adopt VSS, which can be adaptive to the estimation error in each iteration, i.e., large step size is used in the case of large estimation error to accelerate the convergence speed, while small step size is used when the estimation error is small to improve the steady-state estimation accuracy. Simulation results are provided to validate the effectiveness of the proposed scheme.

Includes members:
  Member "non cooperative feedback rate control game for channel state information in wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "qoe oriented resource allocation for 360 degree video transmission over heterogeneous networks")
  Member "online sparse system identification and signal reconstruction using projections onto weighted ell_ 1 balls":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust and computationally efficient anomaly detection using powers of two networks")

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)