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 in an uncertain world mimo covariance matrix optimization with imperfect feedback"
  Abstract: In this paper, we present a distributed learning algorithm for the optimization of signal covariance matrices in Gaussian multiple–input and multiple–output (MIMO) multiple access channel with imperfect (and possibly delayed) feedback. The algorithm is based on the method of matrix exponential learning (MXL) and it has the same information and computation requirements as distributed water-filling. However, unlike water-filling, the proposed algorithm converges to the system’s optimum signal covariance profile even under stochastic uncertainty and imperfect feedback. Moreover, the algorithm also retains its convergence properties in the presence of user update asynchronicities, random delays and/or ergodically changing channel conditions. Our theoretical analysis is complemented by extensive numerical simulations which illustrate the robustness and scalability of MXL in realistic network conditions. In particular, the algorithm retains its convergence speed even for large numbers of users and/or antennas per user.

Connected through hyperedge HE-1 (induced by source paper "online convex optimization and no regret learning algorithms guarantees and applications"):
  with members:
    - "efficient beam alignment in millimeter wave systems using contextual bandits"
    - "distributed stochastic optimization via matrix exponential learning"
    - "a survey on metric learning for feature vectors and structured data"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-2 (induced by source paper "the convergence of machine learning and communications"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "deep learning based mimo communications"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "kernel based adaptive online reconstruction of coverage maps with side information"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "context aware computing for the internet of things a survey"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-3 (induced by source paper "distributed stochastic optimization via matrix exponential learning"):
  with members:
    - "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"
    - "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-4 (induced by source paper "learning to be green robust energy efficiency maximization in dynamic mimo ofdm systems"):
  with members:
    - "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"
    - "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-5 (induced by source paper "dynamic transmit covariance design in mimo fading systems with unknown channel distributions and inaccurate channel state information"):
  with members:
    - "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"
    - "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)