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: "online learning a comprehensive survey"
  Abstract: Online learning represents an important family of machine learning algorithms, in which a learner attempts to resolve an online prediction (or any type of decision-making) task by learning a model/hypothesis from a sequence of data instances one at a time. The goal of online learning is to ensure that the online learner would make a sequence of accurate predictions (or correct decisions) given the knowledge of correct answers to previous prediction or learning tasks and possibly additional information. This is in contrast to many traditional batch learning or offline machine learning algorithms that are often designed to train a model in batch from a given collection of training data instances. This survey aims to provide a comprehensive survey of the online machine learning literatures through a systematic review of basic ideas and key principles and a proper categorization of different algorithms and techniques. Generally speaking, according to the learning type and the forms of feedback information, the existing online learning works can be classified into three major categories: (i) supervised online learning where full feedback information is always available, (ii) online learning with limited feedback, and (iii) unsupervised online learning where there is no feedback available. Due to space limitation, the survey will be mainly focused on the first category, but also briefly cover some basics of the other two categories. Finally, we also discuss some open issues and attempt to shed light on potential future research directions in this field.

Connected through hyperedge HE-1 (induced by source paper "bio inspired digit recognition using reward modulated spike timing dependent plasticity in deep convolutional networks"):
  with members:
    - "deep convolutional spiking neural networks for image classification"
    - "deep learning in spiking neural networks"
    - "multi layered spiking neural network with target timestamp threshold adaptation and stdp"
    - "spyketorch efficient simulation of convolutional spiking neural networks with at most one spike per neuron"
    - "mt spike a multilayer time based spiking neuromorphic architecture with temporal error backpropagation"
    - "optimal localist and distributed coding of spatiotemporal spike patterns through stdp and coincidence detection"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "spatio temporal backpropagation for training high performance spiking neural networks"

Connected through hyperedge HE-2 (induced by source paper "efficient projection free online methods with stochastic recursive gradient"):
  with members:
    - "introduction to online convex optimization"
    - "momentum based variance reduction in non convex sgd"
    - "quantized frank wolfe faster optimization lower communication and projection free"
    - "variance reduced and projection free stochastic optimization"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "block coordinate frank wolfe optimization for structural svms"
    - "projection free online learning"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-3 (induced by source paper "detecting cyberattacks in industrial control systems using online learning algorithms"):
  with members:
    - "intelligent reflecting surface irs enabled covert communications in wireless networks"
    - "cyber insurance for heterogeneous wireless networks"
    - "sol a library for scalable online learning algorithms"
    - "hierarchical cooperation for operator controlled device to device communications a layered coalitional game approach"
    - "economics of internet of things iot an information market approach"
    - "payoff allocation of service coalition in wireless mesh network a cooperative game perspective"
    - "adaptive power management for wireless base stations in a smart grid environment"
    - "exact soft confidence weighted learning"

Connected through hyperedge HE-4 (induced by source paper "stochastic recursive gradient based methods for projection free online learning"):
  with members:
    - "introduction to online convex optimization"
    - "momentum based variance reduction in non convex sgd"
    - "quantized frank wolfe faster optimization lower communication and projection free"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "block coordinate frank wolfe optimization for structural svms"
    - "projection free online learning"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-5 (induced by source paper "adaptive cost sensitive online classification"):
  with members:
    - "efficient second order online learning by sketching"
    - "frequent directions simple and deterministic matrix sketching"
    - "sketching as a tool for numerical linear algebra"
    - "simple and deterministic matrix sketching"
    - "exact soft confidence weighted learning"
    - "sparser johnson lindenstrauss transforms"

Connected through hyperedge HE-6 (induced by source paper "modified online newton step based on element wise multiplication"):
  with members:
    - "saags biased stochastic variance reduction methods for large scale learning"
    - "online newton step algorithm with estimated gradient"

Connected through hyperedge HE-7 (induced by source paper "homogeneous online transfer learning with online distribution discrepancy minimization"):
  with members:
    - "balanced distribution adaptation for transfer learning"
    - "return of frustratingly easy domain adaptation"

Connected through hyperedge HE-8 (induced by source paper "a survey on deep learning for named entity recognition"):
  with members:
    - "learning a unified named entity tagger from multiple partially annotated corpora for efficient adaptation"
    - "a unified mrc framework for named entity recognition"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "a little annotation does a lot of good a study in bootstrapping low resource named entity recognizers"
    - "dependency guided lstm crf for named entity recognition"
    - "hierarchical contextualized representation for named entity recognition"
    - "dice loss for data imbalanced nlp tasks"
    - "attending to entities for better text understanding"

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)