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: "multi scale spectrum sensing in small cell mm wave cognitive wireless networks"
  Abstract: In this paper, a multi-scale approach to spectrum sensing in cognitive cellular networks is proposed. In order to overcome the huge cost incurred in the acquisition of full network state information, a hierarchical scheme is proposed, based on which local state estimates are aggregated up the hierarchy to obtain aggregate state information at multiple scales, which are then sent back to each cell for local decision making. Thus, each cell obtains fine-grained estimates of the channel occupancies of nearby cells, but coarse-grained estimates of those of distant cells. The performance of the aggregation scheme is studied in terms of the trade-off between the throughput achievable by secondary users and the interference generated by the activity of these secondary users to primary users. In order to account for the irregular structure of interference patterns arising from path loss, shadowing, and blockages, which are especially relevant in millimeter wave networks, a greedy algorithm is proposed to find a multi-scale aggregation tree to optimize the performance. It is shown numerically that this tailored hierarchy outperforms a regular tree construction by 60%.

Connected through hyperedge HE-1 (induced by source paper "multi scale spectrum sensing in dense multi cell cognitive networks"):
  with members:
    - "a parallel algorithm for generating a random graph with a prescribed degree sequence"
    - "understanding noise and interference regimes in 5g millimeter wave cellular networks"
    - "investigation of prediction accuracy sensitivity and parameter stability of large scale propagation path loss models for 5g wireless communications"
    - "gains of restricted secondary licensing in millimeter wave cellular systems"
    - "cross layer design of distributed sensing estimation with quality feedback part i optimal schemes"
    - "cross layer estimation and control for cognitive radio exploiting sparse network dynamics"
    - "exploiting device to device communications in joint scheduling of access and backhaul for mmwave small cells"
    - "tractable model for rate in self backhauled millimeter wave cellular networks"

Connected through hyperedge HE-2 (induced by source paper "multi scale spectrum sensing in 5g cognitive networks"):
  with members:
    - "understanding noise and interference regimes in 5g millimeter wave cellular networks"
    - "gains of restricted secondary licensing in millimeter wave cellular systems"
    - "cross layer design of distributed sensing estimation with quality feedback part i optimal schemes"
    - "cross layer estimation and control for cognitive radio exploiting sparse network dynamics"
    - "exploiting device to device communications in joint scheduling of access and backhaul for mmwave small cells"
    - "tractable model for rate in self backhauled millimeter wave cellular networks"
    - "coverage and rate analysis for millimeter wave cellular networks"
    - "what will 5g be"

Connected through hyperedge HE-3 (induced by source paper "throughput optimal beam alignment in millimeter wave networks"):
  with members:
    - "context information for fast cell discovery in mm wave 5g networks"
    - "coverage and rate analysis for millimeter wave cellular networks"
    - "millimeter wave cellular wireless networks potentials and challenges"
    - "millimeter wave channel modeling and cellular capacity evaluation"

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)