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: "optimization of weighted individual energy efficiencies in interference networks"
  Abstract: This paper studies the maximization of the weighted sum energy efficiency (WSEE). We derive a first-order optimal algorithm applicable to a wide class of communication scenarios exhibiting very fast convergence. We also discuss how to leverage monotonic optimization and fractional programming to obtain a global optimal solution at the cost of higher computational complexity. The WSEE of interference networks is studied in detail with an application to relay-assisted multi-cell communication. This scenario is modeled as a non-regenerative multi-way relay channel and the achievable rate region is derived. We apply the proposed algorithm to this scenario and compare its performance to the global optimal algorithm. The results indicate that the proposed algorithm often achieves the global optimal solution and is close to it otherwise. Convergence is achieved within 10 iterations, while the global optimal solution may require more than 106 iterations.

Connected through hyperedge HE-1 (induced by source paper "mixed monotonic programming for fast global optimization"):
  with members:
    - "efficient global optimal resource allocation in non orthogonal interference networks"
    - "energy efficient power control a look at 5g wireless technologies"
    - "resource allocation for energy efficient 3 way relay channels"
    - "robust monotonic optimization framework for multicell miso systems"
    - "achieving global optimality for weighted sum rate maximization in the k user gaussian interference channel with multiple antennas"
    - "framework for link level energy efficiency optimization with informed transmitter"
    - "mapel achieving global optimality for a non convex wireless power control problem"
    - "optimal linear precoding strategies for wideband non cooperative systems based on game theory part ii algorithms"

Connected through hyperedge HE-2 (induced by source paper "efficient global optimal resource allocation in non orthogonal interference networks"):
  with members:
    - "distributed optimization for coordinated beamforming in multicell multigroup multicast systems power minimization and sinr balancing"
    - "energy efficient beam coordination strategies with rate dependent processing power"
    - "energy efficient power control a look at 5g wireless technologies"
    - "optimal achievable rates for interference networks with random codes"
    - "resource allocation for energy efficient 3 way relay channels"
    - "instantaneous relaying for the 3 way relay channel with circular message exchanges"
    - "wireless information and power transfer energy efficiency optimization in ofdma systems"
    - "robust monotonic optimization framework for multicell miso systems"

Connected through hyperedge HE-3 (induced by source paper "deep learning for optimal energy efficient power control in wireless interference networks"):
  with members:
    - "mixed monotonic programming for fast global optimization"
    - "learning to optimize training deep neural networks for interference management"
    - "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-4 (induced by source paper "a globally optimal energy efficient power control framework and its efficient implementation in wireless interference networks"):
  with members:
    - "mixed monotonic programming for fast global optimization"
    - "learning to optimize training deep neural networks for interference management"
    - "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)