human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "multi scale spectrum sensing in 5g cognitive networks"):
  Abstract: A multi-scale approach to spectrum sensing is proposed to overcome the huge energy cost of acquiring full network state information over 5G cognitive networks. Secondary users (SUs) estimate the local spectrum occupancies and aggregate them hierarchically to produce multi-scale estimates. Thus, SUs obtain fine-grained estimates of spectrum occupancies of nearby cells, more relevant to resource allocation tasks, and coarse-grained estimates of those of distant cells. The proposed architecture accounts for local estimation errors, delays in the exchange of state information, as well as irregular interference patterns arising in future fifth-generation (5G) dense cellular systems with irregular cell deployments. An algorithm based on agglomerative clustering is proposed to design an energy-efficient aggregation hierarchy matched to the irregular interference structure, resilient to aggregation delays and local estimation errors. Measuring performance in terms of the trade-off between SU network throughput, interference to PUs and energy efficiency, numerical experiments demonstrate a 10% improvement in SU network throughput over a regular tree construction, for a reference value of interference to PUs, and up to 1/4th of the energy cost needed to acquire full network state information.

Includes members:
  Member "multi scale spectrum sensing in small cell mm wave cognitive wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi scale spectrum sensing in dense multi cell cognitive networks")
  Member "understanding noise and interference regimes in 5g millimeter wave cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint path selection and rate allocation framework for 5g self backhauled mm wave networks")
  Member "gains of restricted secondary licensing in millimeter wave cellular systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stochastic geometry modeling and analysis of finite millimeter wave wireless networks")
  Member "cross layer design of distributed sensing estimation with quality feedback part i optimal schemes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi scale spectrum sensing in dense multi cell cognitive networks")
  Member "cross layer estimation and control for cognitive radio exploiting sparse network dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "thirty years of machine learning the road to pareto optimal next generation wireless networks")
  Member "exploiting device to device communications in joint scheduling of access and backhaul for mmwave small cells":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sub channel allocation for device to device underlaying full duplex mmwave small cells using coalition formation games")
  Member "tractable model for rate in self backhauled millimeter wave cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "joint path selection and rate allocation framework for 5g self backhauled mm wave networks")
  Member "coverage and rate analysis for millimeter wave cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "optimizing information freshness in wireless networks a stochastic geometry approach")

Question: Please tell me which class the source paper 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)