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: "hybrid demodulate forward relay protocol for two way relay channels"
  Abstract: Two-Way Relay Channel (TWRC) plays an important role in relay networks, and efficient relaying protocols are particularly important for this model. However, existing protocols may not be able to realize the potential of TWRC if the two independent fading channels are not carefully handled. In this paper, a Hybrid DeModulate-Forward (HDMF) protocol is proposed to address such a problem. We first introduce the two basic components of HDMF - direct and differential DMF, and then propose the key decision criterion for HDMF based on the corresponding log-likelihood ratios. We further enhance the protocol so that it can be applied independently from the modulation schemes. Through extensive mathematical analysis, theoretical performance of the proposed protocol is investigated. By comparing with existing protocols, the proposed HDMF has lower error rate. A novel scheduling scheme for the proposed protocol is introduced, which has lower length than the benchmark method. The results also reveal the protocol's potential to improve spectrum efficiency of relay channels with unbalanced bilateral traffic.

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)