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: "transmit without regrets online optimization in mimo ofdm cognitive radio systems"
  Abstract: In this paper, we examine cognitive radio systems that evolve dynamically over time due to changing user and environmental conditions. To combine the advantages of orthogonal frequency division multiplexing (OFDM) and multiple-input, multiple-output (MIMO) technologies, we consider a MIMO-OFDM cognitive radio network where wireless users with multiple antennas communicate over several non-interfering frequency bands. As the network's primary users (PUs) come and go in the system, the communication environment changes constantly (and, in many cases, randomly). Accordingly, the network's unlicensed, secondary users (SUs) must adapt their transmit profiles "on the fly" in order to maximize their data rate in a rapidly evolving environment over which they have no control. In this dynamic setting, static solution concepts (such as Nash equilibrium) are no longer relevant, so we focus on dynamic transmit policies that lead to no regret: specifically, we consider policies that perform at least as well as (and typically outperform) even the best fixed transmit profile in hindsight. Drawing on the method of matrix exponential learning and online mirror descent techniques, we derive a no-regret transmit policy for the system's SUs which relies only on local channel state information (CSI). Using this method, the system's SUs are able to track their individually evolving optimum transmit profiles remarkably well, even under rapidly (and randomly) changing conditions. Importantly, the proposed augmented exponential learning (AXL) policy leads to no regret even if the SUs' channel measurements are subject to arbitrarily large observation errors (the imperfect CSI case), thus ensuring the method's robustness in the presence of uncertainties.

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"
    - "learning to be green robust energy efficiency maximization in dynamic mimo ofdm 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"
    - "learning to be green robust energy efficiency maximization in dynamic mimo ofdm 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 "learning in an uncertain world mimo covariance matrix optimization with imperfect feedback"):
  with members:
    - "a parallel decomposition method for nonconvex stochastic multi agent optimization problems"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "distributed learning policies for power allocation in multiple access channels"
    - "regularization techniques for learning with matrices"
    - "the mimo iterative waterfilling algorithm"
    - "pilot contamination and precoding in multi cell tdd systems"

Connected through hyperedge HE-4 (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"
    - "adaptive power allocation and control in time varying multi carrier mimo networks"
    - "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"
    - "energy efficiency optimization for mimo ofdm mobile multimedia communication systems with qos constraints"

Connected through hyperedge HE-5 (induced by source paper "adaptive power allocation and control in time varying multi carrier mimo networks"):
  with members:
    - "joint channel selection and power control in infrastructureless wireless networks a multiplayer multiarmed bandit framework"
    - "resource allocation for power minimization in the downlink of thp based spatial multiplexing mimo ofdma systems"
    - "what will 5g be"
    - "mirror descent meets fixed share and feels no regret"
    - "distributed learning policies for power allocation in multiple access channels"
    - "regularization techniques for learning with matrices"
    - "power allocation games for mimo multiple access channels with coordination"
    - "the mimo iterative waterfilling algorithm"

Connected through hyperedge HE-6 (induced by source paper "in an uncertain world distributed optimization in mimo systems with imperfect information"):
  with members:
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "distributed learning policies for power allocation in multiple access channels"
    - "regularization techniques for learning with matrices"
    - "competitive design of multiuser mimo systems based on game theory a unified view"

Connected through hyperedge HE-7 (induced by source paper "cost efficient throughput maximization in multi carrier cognitive radio systems"):
  with members:
    - "what will 5g be"
    - "distributed learning policies for power allocation in multiple access channels"
    - "dynamic power allocation games in parallel multiple access 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)