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: "tools and benchmarks for automated log parsing"
  Abstract: Logs are imperative in the development and maintenance process of many software systems. They record detailed runtime information that allows developers and support engineers to monitor their systems and dissect anomalous behaviors and errors. The increasing scale and complexity of modern software systems, however, make the volume of logs explodes. In many cases, the traditional way of manual log inspection becomes impractical. Many recent studies, as well as industrial tools, resort to powerful text search and machine learning-based analytics solutions. Due to the unstructured nature of logs, a first crucial step is to parse log messages into structured data for subsequent analysis. In recent years, automated log parsing has been widely studied in both academia and industry, producing a series of log parsers by different techniques. To better understand the characteristics of these log parsers, in this paper, we present a comprehensive evaluation study on automated log parsing and further release the tools and benchmarks for easy reuse. More specifically, we evaluate 13 log parsers on a total of 16 log datasets spanning distributed systems, supercomputers, operating systems, mobile systems, server applications, and standalone software. We report the benchmarking results in terms of accuracy, robustness, and efficiency, which are of practical importance when deploying automated log parsing in production. We also share the success stories and lessons learned in an industrial application at Huawei. We believe that our work could serve as the basis and provide valuable guidance to future research and deployment of automated log parsing.

Connected through hyperedge HE-1 (induced by source paper "self supervised log parsing"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "length matters clustering system log messages using length of words"

Connected through hyperedge HE-2 (induced by source paper "logram efficient log parsing using n gram dictionaries"):
  with members:
    - "a directed acyclic graph approach to online log parsing"
    - "length matters clustering system log messages using length of words"
    - "the making of cloud applications an empirical study on software development for the cloud"

Connected through hyperedge HE-3 (induced by source paper "software logging for machine learning"):
  with members:
    - "software engineering challenges of deep learning"

Connected through hyperedge HE-4 (induced by source paper "log message anomaly detection and classification using auto b lstm and auto gru"):
  with members:
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "regularizing deep neural networks by noise its interpretation and optimization"
    - "text classification improved by integrating bidirectional lstm with two dimensional max pooling"
    - "long short term memory based recurrent neural network architectures for large vocabulary speech recognition"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "recurrent neural network regularization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "oversampling log messages using a sequence generative adversarial network for anomaly detection and classification"):
  with members:
    - "generating diverse and accurate visual captions by comparative adversarial learning"
    - "regularizing deep neural networks by noise its interpretation and optimization"
    - "generative adversarial network for abstractive text summarization"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-6 (induced by source paper "a pvalue guided anomaly detection approach combining multiple heterogeneous log parser algorithms on iiot systems"):
  with members:
    - "anomaly detection using autoencoders in high performance computing systems"
    - "a directed acyclic graph approach to online log parsing"
    - "plug in martingales for testing exchangeability on line"
    - "a tutorial on conformal prediction"

Connected through hyperedge HE-7 (induced by source paper "logzip extracting hidden structures via iterative clustering for log compression"):
  with members:
    - "a directed acyclic graph approach to online log parsing"
    - "detection of early stage enterprise infection by mining large scale log data"
    - "the unified logging infrastructure for data analytics at twitter"

Connected through hyperedge HE-8 (induced by source paper "delog a privacy preserving log filtering framework for online compute platforms"):
  with members:
    - "a directed acyclic graph approach to online log parsing"

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.SE(Software Engineering)