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: "adaptive power allocation and control in time varying multi carrier mimo networks"
  Abstract: In this paper, we examine the fundamental trade-off between radiated power and achieved throughput in wireless multi-carrier, multiple-input and multiple-output (MIMO) systems that vary with time in an unpredictable fashion (e.g. due to changes in the wireless medium or the users' QoS requirements). Contrary to the static/stationary channel regime, there is no optimal power allocation profile to target (either static or in the mean), so the system's users must adapt to changes in the environment "on the fly", without being able to predict the system's evolution ahead of time. In this dynamic context, we formulate the users' power/throughput trade-off as an online optimization problem and we provide a matrix exponential learning algorithm that leads to no regret - i.e. the proposed transmit policy is asymptotically optimal in hindsight, irrespective of how the system evolves over time. Furthermore, we also examine the robustness of the proposed algorithm under imperfect channel state information (CSI) and we show that it retains its regret minimization properties under very mild conditions on the measurement noise statistics. As a result, users are able to track the evolution of their individually optimum transmit profiles remarkably well, even under rapidly changing network conditions and high uncertainty. Our theoretical analysis is validated by extensive numerical simulations corresponding to a realistic network deployment and providing further insights in the practical implementation aspects of the proposed algorithm.

Connected through hyperedge HE-1 (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"
    - "learning to be green robust energy efficiency maximization in dynamic mimo ofdm systems"
    - "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-2 (induced by source paper "learning to be green robust energy efficiency maximization in dynamic mimo ofdm systems"):
  with members:
    - "learning in an uncertain world mimo covariance matrix optimization with imperfect feedback"
    - "energy efficient precoder design for mimo ofdm with rate dependent circuit power"
    - "energy aware competitive power allocation for heterogeneous networks under qos constraints"
    - "joint channel selection and power control in infrastructureless wireless networks a multiplayer multiarmed bandit framework"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"
    - "transmit without regrets online optimization in mimo ofdm cognitive radio systems"
    - "energy efficiency optimization for mimo ofdm mobile multimedia communication systems with qos constraints"

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"
    - "learning to be green robust energy efficiency maximization in dynamic mimo ofdm systems"
    - "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)