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: "navigating the data lake with datamaran automatically extracting structure from log datasets"
  Abstract: Organizations routinely accumulate semi-structured log datasets generated as the output of code; these datasets remain unused and uninterpreted, and occupy wasted space - this phenomenon has been colloquially referred to as "data lake" problem. One approach to leverage these semi-structured datasets is to convert them into a structured relational format, following which they can be analyzed in conjunction with other datasets. We present Datamaran, an tool that extracts structure from semi-structured log datasets with no human supervision. Datamaran automatically identifies field and record endpoints, separates the structured parts from the unstructured noise or formatting, and can tease apart multiple structures from within a dataset, in order to efficiently extract structured relational datasets from semi-structured log datasets, at scale with high accuracy. Compared to other unsupervised log dataset extraction tools developed in prior work, Datamaran does not require the record boundaries to be known beforehand, making it much more applicable to the noisy log files that are ubiquitous in data lakes. Datamaran can successfully extract structured information from all datasets used in prior work, and can achieve 95% extraction accuracy on automatically collected log datasets from GitHub - a substantial 66% increase of accuracy compared to unsupervised schemes from prior work. Our user study further demonstrates that the extraction results of Datamaran are closer to the desired structure than competing algorithms.

Connected through hyperedge HE-1 (induced by source paper "dataset search a survey"):
  with members:
    - "interactive summarization and exploration of top aggregate query answers"
    - "ad hoc table retrieval using semantic similarity"
    - "cuttlefish a lightweight primitive for adaptive query processing"
    - "on the fly table generation"
    - "everything you always wanted to know about a dataset studies in data summarisation"
    - "a survey on data collection for machine learning a big data ai integration perspective"
    - "neural vector spaces for unsupervised information retrieval"
    - "entitables smart assistance for entity focused tables"

Connected through hyperedge HE-2 (induced by source paper "rumble data independence when data is in a mess"):
  with members:
    - "apache hive from mapreduce to enterprise grade big data warehousing"
    - "automatically leveraging mapreduce frameworks for data intensive applications"
    - "ray a distributed framework for emerging ai applications"
    - "asterixdb a scalable open source bdms"
    - "shark sql and rich analytics at scale"

Connected through hyperedge HE-3 (induced by source paper "a survey on data collection for machine learning a big data ai integration perspective"):
  with members:
    - "data cleaning for accurate fair and robust models a big data ai integration approach"
    - "the gan landscape losses architectures regularization and normalization"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "snorkel drybell a case study in deploying weak supervision at industrial scale"
    - "data synthesis based on generative adversarial networks"
    - "quality control in crowdsourcing a survey of quality attributes assessment techniques and assurance actions"
    - "a survey on deep transfer learning"
    - "learning based video motion magnification"

Connected through hyperedge HE-4 (induced by source paper "tools and benchmarks for automated log parsing"):
  with members:
    - "length matters clustering system log messages using length of words"
    - "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-5 (induced by source paper "clx towards verifiable pbe data transformation"):
  with members:
    - "robustfill neural program learning under noisy i o"

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.DB(Databases)