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: "a brief introduction to machine learning for engineers"
  Abstract: This monograph aims at providing an introduction to key concepts, algorithms, and theoretical frameworks in machine learning, including supervised and unsupervised learning, statistical learning theory, probabilistic graphical models and approximate inference. The intended readership consists of electrical engineers with a background in probability and linear algebra. The treatment builds on first principles, and organizes the main ideas according to clearly defined categories, such as discriminative and generative models, frequentist and Bayesian approaches, exact and approximate inference, directed and undirected models, and convex and non-convex optimization. The mathematical framework uses information-theoretic measures as a unifying tool. The text offers simple and reproducible numerical examples providing insights into key motivations and conclusions. Rather than providing exhaustive details on the existing myriad solutions in each specific category, for which the reader is referred to textbooks and papers, this monograph is meant as an entry point for an engineer into the literature on machine learning.

Connected through hyperedge HE-1 (induced by source paper "from learning to meta learning reduced training overhead and complexity for communication systems"):
  with members:
    - "deep transfer learning based downlink channel prediction for fdd massive mimo systems"
    - "meta learning with warped gradient descent"
    - "meta learning without memorization"
    - "modular meta learning with shrinkage"
    - "uncertainty in model agnostic meta learning using variational inference"
    - "on the convergence theory of gradient based model agnostic meta learning algorithms"
    - "on first order meta learning algorithms"
    - "recasting gradient based meta learning as hierarchical bayes"

Connected through hyperedge HE-2 (induced by source paper "margin maximization as lossless maximal compression"):
  with members:
    - "training behavior of deep neural network in frequency domain"
    - "chaining mutual information and tightening generalization bounds"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "opening the black box of deep neural networks via information"
    - "deep variational information bottleneck"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-3 (induced by source paper "an introduction to probabilistic spiking neural networks probabilistic models learning rules and applications"):
  with members:
    - "learning first to spike policies for neuromorphic control using policy gradients"
    - "surrogate gradient learning in spiking neural networks"
    - "low power neuromorphic hardware for signal processing applications"
    - "training dynamic exponential family models with causal and lateral dependencies for generalized neuromorphic computing"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "benchmarking keyword spotting efficiency on neuromorphic hardware"
    - "boltzmann machines for time series"

Connected through hyperedge HE-4 (induced by source paper "information centric grant free access for iot fog networks edge vs cloud detection and learning"):
  with members:
    - "start making sense semantic plane filtering and control for post 5g connectivity"
    - "cooperative deep reinforcement learning for multiple group nb iot networks optimization"
    - "non orthogonal multiplexing of ultra reliable and broadband services in fog radio architectures"
    - "deep joint source channel coding for wireless image transmission"
    - "5g wireless network slicing for embb urllc and mmtc a communication theoretic view"
    - "towards massive connectivity support for scalable mmtc communications in 5g networks"
    - "fog aided wireless networks for content delivery fundamental latency tradeoffs"
    - "fog computing based radio access networks issues and challenges"

Connected through hyperedge HE-5 (induced by source paper "tensorscone a secure tensorflow framework using intel sgx"):
  with members:
    - "slick secure middleboxes using shielded execution"
    - "privacy preserving stream analytics the marriage of randomized response and approximate computing"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"
    - "empirical evaluation of rectified activations in convolutional network"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-6 (induced by source paper "optimizing pipelined computation and communication for latency constrained edge learning"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "wireless network intelligence at the edge"
    - "adaptive task allocation for mobile edge learning"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "federated multi task learning"
    - "federated learning of deep networks using model averaging"

Connected through hyperedge HE-7 (induced by source paper "itene intrinsic transfer entropy neural estimator"):
  with members:
    - "understanding the limitations of variational mutual information estimators"
    - "ccmi classifier based conditional mutual information estimation"
    - "jidt an information theoretic toolkit for studying the dynamics of complex systems"
    - "universal estimation of directed information"
    - "interpretations of directed information in portfolio theory data compression and hypothesis testing"

Connected through hyperedge HE-8 (induced by source paper "an information theoretic model for summarization and some basic results"):
  with members:
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "bottom up abstractive summarization"
    - "get to the point summarization with pointer generator networks"
    - "learning mixtures of submodular shells with application to document summarization"

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.LG(Machine Learning)