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: "learning to be green robust energy efficiency maximization in dynamic mimo ofdm systems"
  Abstract: In this paper, we examine the maximization of energy efficiency (EE) in next-generation multi-user MIMO-OFDM networks that evolve dynamically over time - e.g. due to user mobility, fluctuations in the wireless medium, modulations in the users' load, etc. Contrary to the static/stationary regime, the system may evolve in an arbitrary manner so, targeting a fixed optimum state (either static or in the mean) becomes obsolete; instead, users must adjust to changes in the system "on the fly", without being able to predict the state of the system in advance. To tackle these issues, we propose a simple and distributed online optimization policy that leads to no regret, i.e. it allows users to match (and typically outperform) even the best fixed transmit policy in hindsight, irrespective of how the system varies with time. Moreover, to account for the scarcity of perfect channel state information (CSI) in massive MIMO systems, we also study the algorithm's robustness in the presence of measurement errors and observation noise. Importantly, the proposed policy retains its no-regret properties under very mild assumptions on the error statistics and, on average, it enjoys the same performance guarantees as in the noiseless, deterministic case. Our analysis is supplemented by extensive numerical simulations which show that, in realistic network environments, users track their individually optimum transmit profile even under rapidly changing channel conditions, achieving gains of up to 600% in energy efficiency over uniform power allocation policies.

Connected through hyperedge HE-1 (induced by source paper "a learning approach for low complexity optimization of energy efficiency in multicarrier wireless networks"):
  with members:
    - "a brief introduction to machine learning for engineers"
    - "energy efficient power control a look at 5g wireless technologies"
    - "a survey of energy efficient techniques for 5g networks and challenges ahead"
    - "transmit without regrets online optimization in mimo ofdm cognitive radio systems"
    - "distributed learning policies for power allocation in multiple access channels"
    - "framework for link level energy efficiency optimization with informed transmitter"
    - "energy efficient power control in impulse radio uwb wireless networks"

Connected through hyperedge HE-2 (induced by source paper "distributed stochastic optimization via matrix exponential learning"):
  with members:
    - "learning in an uncertain world mimo covariance matrix optimization with imperfect feedback"
    - "energy efficient power control a look at 5g wireless technologies"
    - "adaptive power allocation and control in time varying multi carrier mimo networks"
    - "transmit without regrets online optimization in mimo ofdm cognitive radio systems"
    - "optimal design of energy efficient multi user mimo systems is massive mimo the answer"
    - "a survey on metric learning for feature vectors and structured data"
    - "regularization techniques for learning with matrices"

Connected through hyperedge HE-3 (induced by source paper "dynamic transmit covariance design in mimo fading systems with unknown channel distributions and inaccurate channel state information"):
  with members:
    - "learning in an uncertain world mimo covariance matrix optimization with imperfect feedback"
    - "adaptive power allocation and control in time varying multi carrier mimo networks"
    - "capacity achieving rateless polar codes"
    - "rateless coding for gaussian 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)