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: "asterixdb a scalable open source bdms"
  Abstract: AsterixDB is a new, full-function BDMS (Big Data Management System) with a feature set that distinguishes it from other platforms in today's open source Big Data ecosystem. Its features make it well-suited to applications like web data warehousing, social data storage and analysis, and other use cases related to Big Data. AsterixDB has a flexible NoSQL style data model; a query language that supports a wide range of queries; a scalable runtime; partitioned, LSM-based data storage and indexing (including B+-tree, R-tree, and text indexes); support for external as well as natively stored data; a rich set of built-in types; support for fuzzy, spatial, and temporal types and queries; a built-in notion of data feeds for ingestion of data; and transaction support akin to that of a NoSQL store. #R##N#Development of AsterixDB began in 2009 and led to a mid-2013 initial open source release. This paper is the first complete description of the resulting open source AsterixDB system. Covered herein are the system's data model, its query language, and its software architecture. Also included are a summary of the current status of the project and a first glimpse into how AsterixDB performs when compared to alternative technologies, including a parallel relational DBMS, a popular NoSQL store, and a popular Hadoop-based SQL data analytics platform, for things that both technologies can do. Also included is a brief description of some initial trials that the system has undergone and the lessons learned (and plans laid) based on those early "customer" engagements.

Connected through hyperedge HE-1 (induced by source paper "lsm based storage techniques a survey"):
  with members:
    - "efficient data ingestion and query processing for lsm based storage systems"
    - "bigtable merge compaction"
    - "don t thrash how to cache your hash on flash"
    - "optimal aggregation algorithms for middleware"

Connected through hyperedge HE-2 (induced by source paper "bad to the bone big active data at its core"):
  with members:
    - "an idea an ingestion framework for data enrichment in asterixdb"
    - "efficient data ingestion and query processing for lsm based storage systems"
    - "planetsense a real time streaming and spatio temporal analytics platform for gathering geo spatial intelligence from open source data"
    - "linview incremental view maintenance for complex analytical queries"

Connected through hyperedge HE-3 (induced by source paper "big data analytics for large scale wireless networks challenges and opportunities"):
  with members:
    - "blockchain for internet of things a survey"
    - "big data analytics for manufacturing internet of things opportunities challenges and enabling technologies"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "stealing hyperparameters in machine learning"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "a survey of multi objective optimization in wireless sensor networks metrics algorithms and open problems"

Connected through hyperedge HE-4 (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"
    - "navigating the data lake with datamaran automatically extracting structure from log datasets"
    - "shark sql and rich analytics at scale"

Connected through hyperedge HE-5 (induced by source paper "on performance stability in lsm based storage systems extended version"):
  with members:
    - "lsm based storage techniques a survey"
    - "an idea an ingestion framework for data enrichment in asterixdb"
    - "efficient data ingestion and query processing for lsm based storage systems"
    - "enabling efficient updates in kv storage via hashing design and performance evaluation"

Connected through hyperedge HE-6 (induced by source paper "an lsm based tuple compaction framework for apache asterixdb"):
  with members:
    - "lsm based storage techniques a survey"
    - "efficient data ingestion and query processing for lsm based storage systems"
    - "the sql query language configurable unifying and semi structured"

Connected through hyperedge HE-7 (induced by source paper "aframe extending dataframes for large scale modern data analysis extended version"):
  with members:
    - "ray a distributed framework for emerging ai applications"
    - "mllib machine learning in apache spark"

Connected through hyperedge HE-8 (induced by source paper "on performance stability in lsm based storage systems"):
  with members:
    - "piql success tolerant query processing in the cloud"

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)