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: "an outage exponent region based coded f matching framework for channel allocation in multi carrier multi access channels"
  Abstract: The multi-carrier multi-access technique is widely adopt in future wireless communication systems, such as IEEE 802.16m and 3GPP LTE-A. The channel resources allocation in multi-carrier multi-access channel, which can greatly improve the system throughput with QoS assurance, thus attracted much attention from both academia and industry. There lacks, however, an analytic framework with a comprehensive performance metric, such that it is difficult to fully exploit the potentials of channel allocation. This paper will propose an analytic coded fmatching framework, where the outage exponent region (OER) will be defined as the performance metric. The OER determines the relationship of the outage performance among all of the users in the full SNR range, and converges to the diversity-multiplexing region (DMR) when SNR tends to infinity. To achieve the optimal OER and DMR, the random bipartite graph (RBG) approach, only depending on 1 bit CSI, will be proposed to formulate this problem. Based on the RBG formulation, the optimal frequency-domain coding based maximum f-matching method is then proposed. By analyzing the combinatorial structure of the RBG based coded f-matching with the help of saddlepoint approximation, the outage probability of each user, OER, and DMR will be derived in closed-form formulas. It will be shown that all of the users share the total multiplexing gain according to their rate requirements, while achieving the full frequency diversity, i.e., the optimal OER and DMR. Based on the principle of parallel computations, the parallel vertices expansion & random rotation based Hopcroft-Karp (PVER2HK) algorithm, which enjoys a logarithmic polynomial complexity, will be proposed. The simulation results will not only verify the theoretical derivations, but also show the significant performance gains.

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)