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: "on network simplification for gaussian half duplex diamond networks"
  Abstract: This paper investigates the simplification problem in Gaussian Half-Duplex (HD) diamond networks. The goal is to answer the following question: what is the minimum (worst-case) fraction of the total HD capacity that one can always achieve by smartly selecting a subset of k relays, out of the N possible ones? We make progress on this problem for k = 1 and k = 2 and show that for N = k + 1, k ∈ |1, 2} at least k/k+1 of the total HD capacity is always approximately (i.e., up to a constant gap) achieved. Interestingly, and differently from the Full-Duplex (FD) case, the ratio in HD depends on N, and decreases as N increases. For all values of N and k for which we derive worst case fractions, we also show these to be approximately tight. This is accomplished by presenting N-relay Gaussian HD diamond networks for which the best k-relay subnetwork has an approximate HD capacity equal to the worst-case fraction of the total approximate HD capacity. Moreover, we provide additional comparisons between the performance of this simplification problem for HD and FD networks, which highlight their different natures.

Connected through hyperedge HE-1 (induced by source paper "network simplification in half duplex building on submodularity"):
  with members:
    - "on the impact of a single edge on the network coding capacity"
    - "cut set bound is loose for gaussian relay networks"
    - "the approximate optimality of simple schedules for half duplex multi relay networks"
    - "gaussian half duplex relay networks improved constant gap and connections with the assignment problem"
    - "wireless network simplification the gaussian n relay diamond network"

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)