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: "ad hoc networking with cost effective infrastructure generalized capacity scaling"
  Abstract: Capacity scaling of a large hybrid network with unit node density, consisting of $n$ wireless ad hoc nodes, base stations (BSs) equipped with multiple antennas, and one remote central processor (RCP), is analyzed when wired backhaul links between the BSs and the RCP are rate-limited. We deal with a general scenario where the number of BSs, the number of antennas at each BS, and the backhaul link rate can scale at arbitrary rates relative to $n$ (i.e., we introduce three scaling parameters). We first derive the minimum backhaul link rate required to achieve the same capacity scaling law as in the infinite-capacity backhaul link case. Assuming an arbitrary rate scaling of each backhaul link, a generalized achievable throughput scaling law is then analyzed in the network based on using one of pure multihop, hierarchical cooperation, and two infrastructure-supported routing protocols, and moreover, three-dimensional information-theoretic operating regimes are explicitly identified according to the three scaling parameters. In particular, we show the case where our network having a power limitation is also fundamentally in the degrees-of-freedom- or infrastructure-limited regime, or both. In addition, a generalized cut-set upper bound under the network model is derived by cutting not only the wireless connections but also the wired connections. It is shown that our upper bound matches the achievable throughput scaling even under realistic network conditions such that each backhaul link rate scales slower than the aforementioned minimum-required backhaul link rate.

Connected through hyperedge HE-1 (induced by source paper "capacity scaling of cellular networks impact of bandwidth infrastructure density and number of antennas"):
  with members:
    - "unified capacity limit of non coherent wideband fading channels"
    - "understanding noise and interference regimes in 5g millimeter wave cellular networks"
    - "spectrum sharing for device to device communication in cellular networks"
    - "demystifying the scaling laws of dense wireless networks no linear scaling in practice"
    - "tractable model for rate in self backhauled millimeter wave cellular networks"
    - "massive mimo for next generation wireless systems"
    - "improving third party relaying for lte a a realistic simulation approach"
    - "scaling laws for infrastructure single and multihop wireless networks in wideband regimes"

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)