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: "large scale spectrum allocation for cellular networks via sparse optimization"
  Abstract: This paper studies joint spectrum allocation and user association in large heterogeneous cellular networks. The objective is to maximize some network utility function based on given traffic statistics collected over a slow timescale, conceived to be seconds to minutes. A key challenge is scalability: interference across cells creates dependencies across the entire network, making the optimization problem computationally challenging as the size of the network becomes large. A suboptimal solution is presented, which performs well in networks consisting of 100 access points (APs) serving several hundred user devices. This is achieved by optimizing over local overlapping neighborhoods, defined by interference conditions, and by exploiting the sparsity of a globally optimal solution. Specifically, with a total of   $k$   user devices in the entire network, it suffices to divide the spectrum into   $k$    segments, where each segment is mapped to a particular set, or  pattern , of active APs within each local neighborhood. The problem is then to find a mapping of segments to patterns, and to optimize the widths of the segments. A convex relaxation is proposed for this, which relies on a reweighted   $\ell _1$   approximation of an    $\ell _0$   constraint, and is used to enforce the mapping of a unique pattern to each spectrum segment. A distributed implementation based on alternating direction method of multipliers is also proposed. Numerical comparisons with benchmark schemes show that the proposed method achieves a substantial increase in achievable throughput and/or reduction in the average packet delay.

Connected through hyperedge HE-1 (induced by source paper "resource allocation in green dense cellular networks complexity and algorithms"):
  with members:
    - "optimal user scheduling in energy harvesting wireless networks"
    - "scalable spectrum allocation and user association in networks with many small cells"
    - "boost base station on off switching strategy for energy efficient massive mimo hetnets"
    - "online ski rental for on off scheduling of energy harvesting base stations"
    - "online energy efficient packet scheduling for a common deadline with and without energy harvesting"
    - "gate greening at the edges"
    - "energy efficient cell activation user association and spectrum allocation in heterogeneous networks"
    - "optimizing user association and spectrum allocation in hetnets a utility perspective"

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)