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: "linear capacity scaling in wireless networks beyond physical limits"
  Abstract: We investigate the role of cooperation in wireless networks subject to a spatial degrees of freedom limitation. To address the worst case scenario, we consider a free-space line-of-sight type environment with no scattering and no fading. We identify three qualitatively different operating regimes that are determined by how the area of the network A, normalized with respect to the wavelength λ, compares to the number of users n. In networks with √A/λ ≤ √n, the limitation in spatial degrees of freedom does not allow to achieve a capacity scaling better than √n and this performance can be readily achieved by multi-hopping. This result has been recently shown in [7]. However, for networks with √A/λ > √n the number of available degrees of freedom is min (n, √A/λ), larger that what can be achieved by multi-hopping. We show that the optimal capacity scaling in this regime is achieved by hierarchical cooperation. In particular, in networks with √A/λ > √n, hierarchical cooperation can achieve linear scaling.

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"

Connected through hyperedge HE-2 (induced by source paper "on the order optimality of large scale underwater networks"):
  with members:
    - "improved capacity scaling in wireless networks with infrastructure"
    - "capacity scaling laws for underwater networks"
    - "on capacity scaling in arbitrary wireless networks"
    - "information theoretic operating regimes of large wireless networks"
    - "on the relationship between transmission power and capacity of an underwater acoustic communication channel"
    - "underwater acoustic networks channel models and network coding based lower bound to transmission power for multicast"
    - "hierarchical cooperation achieves optimal capacity scaling in ad hoc networks"

Connected through hyperedge HE-3 (induced by source paper "information theoretic capacity of clustered random networks"):
  with members:
    - "on capacity scaling in arbitrary wireless networks"
    - "information theoretic operating regimes of large wireless networks"
    - "hierarchical cooperation achieves optimal capacity scaling in ad hoc networks"

Connected through hyperedge HE-4 (induced by source paper "capacity scaling of wireless ad hoc networks shannon meets maxwell"):
  with members:
    - "hierarchical cooperation achieves optimal capacity scaling in ad hoc networks"

Connected through hyperedge HE-5 (induced by source paper "caching in wireless networks"):
  with members:
    - "optimality and approximate optimality of source channel separation in networks"
    - "the balanced unicast and multicast capacity regions of large wireless networks"
    - "on capacity scaling in arbitrary wireless networks"
    - "hierarchical cooperation achieves optimal capacity scaling in ad hoc networks"
    - "network information flow with correlated sources"

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)