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: "mixed monotonic programming for fast global optimization"
  Abstract: While globally optimal solutions to convex programs can be computed efficiently in polynomial time, this is, in general, not possible for nonconvex optimization problems. Therefore, locally optimal approaches or other efficient suboptimal heuristics are usually applied for practical implementations. However, there is also a strong interest in computing globally optimal solutions of nonconvex problems in offline simulations in order to benchmark the faster suboptimal algorithms. Global solutions often rely on monotonicity properties. A common approach is to reformulate problems into a canonical form of a monotonic optimization problem, where the monotonicity becomes evident, but this often comes at the cost of nested optimizations, increased numbers of variables, and/or slow convergence. The framework of mixed monotonic programming (MMP) proposed in this paper is a more direct approach that exploits hidden monotonicity properties without performance-deteriorating reformulations. By means of a wide range of application examples from the area of signal processing for communications (including energy efficiency for green communications, resource allocation in interference networks, scheduling for fairness and quality of service, as well as beamformer design in multiantenna systems), we demonstrate that the novel MMP approach leads to tremendous complexity reductions compared to state-of-the-art methods for global optimization.

Connected through hyperedge HE-1 (induced by source paper "two user simo interference channel with tin improper signaling versus time sharing"):
  with members:
    - "improper signaling versus time sharing in the two user gaussian interference channel with tin"
    - "rate region boundary of the siso z interference channel with improper signaling"
    - "optimized transmission with improper gaussian signaling in the k user miso interference channel"
    - "transmit optimization with improper gaussian signaling for interference channels"
    - "achieving global optimality for weighted sum rate maximization in the k user gaussian interference channel with multiple antennas"
    - "interference alignment with asymmetric complex signaling settling the host madsen nosratinia conjecture"

Connected through hyperedge HE-2 (induced by source paper "deep learning for optimal energy efficient power control in wireless interference networks"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "optimization of weighted individual energy efficiencies in interference networks"
    - "an introduction to deep learning for the physical layer"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "intelligent power control for spectrum sharing a deep reinforcement learning approach"
    - "echo state networks for proactive caching in cloud based radio access networks with mobile users"
    - "learning radio resource management in 5g networks framework opportunities and challenges"

Connected through hyperedge HE-3 (induced by source paper "a globally optimal energy efficient power control framework and its efficient implementation in wireless interference networks"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "optimization of weighted individual energy efficiencies in interference networks"
    - "an introduction to deep learning for the physical layer"
    - "energy efficient power control a look at 5g wireless technologies"
    - "a survey of energy efficient techniques for 5g networks and challenges ahead"
    - "energy efficiency optimization for mimo broadcast channels"

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)