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: "code frequency block group coding for anti spoofing pilot authentication in multi antenna ofdm systems"
  Abstract: A pilot spoofer can paralyze the channel estimation in multi-user orthogonal frequency-division multiplexing (OFD- M) systems by using the same publicly-known pilot tones as legitimate nodes. This causes the problem of pilot authentication (PA). To solve this, we propose, for a two-user multi-antenna OFDM system, a code-frequency block group (CFBG) coding based PA mechanism. Here multi-user pilot information, after being randomized independently to avoid being spoofed, are converted into activation patterns of subcarrier-block groups on code-frequency domain. Those patterns, though overlapped and interfered mutually in the wireless transmission environment, are qualified to be separated and identified as the original pilots with high accuracy, by exploiting CFBG coding theory and channel characteristic. Particularly, we develop the CFBG code through two steps, i.e., 1) devising an ordered signal detection technique to recognize the number of signals coexisting on each subcarrier block, and encoding each subcarrier block with the detected number; 2) constructing a zero-false-drop (ZFD) code and block detection based (BD) code via k-dimensional Latin hypercubes and integrating those two codes into the CFBG code. This code can bring a desirable pilot separation error probability (SEP), inversely proportional to the number of occupied subcarriers and antennas with a power of k. To apply the code to PA, a scheme of pilot conveying, separation and identification is proposed. Based on this novel PA, a joint channel estimation and identification mechanism is proposed to achieve high-precision channel recovery and simultaneously enhance PA without occupying extra resources. Simulation results verify the effectiveness of our proposed mechanism.

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)