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 dense multi cell cognitive networks"):
  Abstract: Multi-scale spectrum sensing is proposed to overcome the cost of full network state information on the spectrum occupancy of primary users (PUs) in dense multi-cell cognitive networks. Secondary users (SUs) estimate the local spectrum occupancies and aggregate them hierarchically to estimate spectrum occupancy at multiple spatial scales. Thus, SUs obtain fine-grained estimates of spectrum occupancies of nearby cells, more relevant to scheduling tasks, and coarse-grained estimates of those of distant cells. An agglomerative clustering algorithm is proposed to design a cost-effective aggregation tree, matched to the structure of interference, robust to local estimation errors and delays. Given these multi-scale estimates, the SU traffic is adapted in a decentralized fashion in each cell, to optimize the trade-off among SU cell throughput, interference caused to PUs, and mutual SU interference. Numerical evaluations demonstrate a small degradation in SU cell throughput (up to 15% for a 0dB interference-to-noise ratio experienced at PUs) compared to a scheme with full network state information, using only one-third of the cost incurred in the exchange of spectrum estimates. The proposed interference-matched design is shown to significantly outperform a random tree design, by providing more relevant information for network control, and a state-of-the-art consensus-based algorithm, which does not leverage the spatio-temporal structure of interference across the network.

Includes members:
  Member "a parallel algorithm for generating a random graph with a prescribed degree sequence":
    also connected through other hyperedges:
      (none)
  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 5g 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 "investigation of prediction accuracy sensitivity and parameter stability of large scale propagation path loss models for 5g wireless communications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a millimeter wave channel simulator nyusim with spatial consistency and human blockage")
  Member "gains of restricted secondary licensing in millimeter wave cellular systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "multi scale spectrum sensing in 5g cognitive networks")
  Member "cross layer estimation and control for cognitive radio exploiting sparse network dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "sub channel allocation for device to device underlaying full duplex mmwave small cells using coalition formation games")

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)