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: "blind channel estimation and data detection with unknown modulation and coding scheme"
  Abstract: This paper investigates a complete blind receiver approach in an unknown multipath fading channel, which has multiple tasks including blind channel estimation, noise power estimation, modulation classification, channel coding recognition, and data detection. Each of these tasks has been sufficiently studied in the literature. However, to the best of our knowledge, this overall problem has not been investigated previously. This paper is the first attempt to address this overall problem jointly. We propose a complete blind receiver approach that jointly estimates the unknown channel state information and noise power, recognizes the unknown modulation and coding scheme, detects the data of interest, and thus named BERD receiver. Another merit of the proposed BERD receiver is that it can be implemented for both a single receiver and multiple receivers, which ensures successful estimation, recognition, and detection for such an extremely difficult problem. In addition, numerical results show the performance of the proposed receiver in three folds: a) the BERD receiver outperforms the linear minimum mean squared error (LMMSE) pilot-based channel estimator by over 3.5 dB at the MSE of 0.01; b) the correct modulation/coding recognition performance of the BERD receiver is within 0.3 dB as close to the recognition benchmark when the perfect channel state information (CSI) is available; c) the BERD receiver is within 0.5 dB at the bit error rate of 0.001 compared to the benchmark when the modulation, the channel coding, and the CSI are perfectly known. Finally, the BERD receiver finds many applications in both civilian and military scenarios, such as the interference cancelation in spectrum sharing, real-time signal interception, and processing in electronic warfare operations, automatic recognition of a detect signal in software-defined radio.

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)