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: "shark sql and rich analytics at scale"
  Abstract: Shark is a new data analysis system that marries query processing with complex analytics on large clusters. It leverages a novel distributed memory abstraction to provide a unified engine that can run SQL queries and sophisticated analytics functions (e.g., iterative machine learning) at scale, and efficiently recovers from failures mid-query. This allows Shark to run SQL queries up to 100x faster than Apache Hive, and machine learning programs up to 100x faster than Hadoop. Unlike previous systems, Shark shows that it is possible to achieve these speedups while retaining a MapReduce-like execution engine, and the fine-grained fault tolerance properties that such engines provide. It extends such an engine in several ways, including column-oriented in-memory storage and dynamic mid-query replanning, to effectively execute SQL. The result is a system that matches the speedups reported for MPP analytic databases over MapReduce, while offering fault tolerance properties and complex analytics capabilities that they lack.

Connected through hyperedge HE-1 (induced by source paper "knowledge graphs"):
  with members:
    - "modl a modular ontology design library"
    - "drum end to end differentiable rule mining on knowledge graphs"
    - "hypernetwork knowledge graph embeddings"
    - "ceres distantly supervised relation extraction from the semi structured web"
    - "a tutorial on modular ontology modeling with ontology design patterns the cooking recipes ontology"
    - "building an ecosystem for the tyrolean tourism knowledge graph"
    - "eventkg a multilingual event centric temporal knowledge graph"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "providing insights for queries affected by failures and stragglers"):
  with members:
    - "blinkdb queries with bounded errors and bounded response times on very large data"
    - "fdb a query engine for factorised relational databases"

Connected through hyperedge HE-3 (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"
    - "asterixdb a scalable open source bdms"

Connected through hyperedge HE-4 (induced by source paper "whiz a fast and flexible data analytics system"):
  with members:
    - "occupy the cloud distributed computing for the 99"
    - "mllib machine learning in apache spark"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-5 (induced by source paper "terra scalable cross layer gda optimizations"):
  with members:
    - "dccast efficient point to multipoint transfers across datacenters"
    - "finishing flows quickly with preemptive scheduling"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

Connected through hyperedge HE-6 (induced by source paper "a survey on spark ecosystem for big data processing"):
  with members:
    - "sparkle optimizing spark for large memory machines and analytics"
    - "deepspark a spark based distributed deep learning framework for commodity clusters"
    - "keystoneml optimizing pipelines for large scale advanced analytics"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "mllib machine learning in apache spark"
    - "splash user friendly programming interface for parallelizing stochastic algorithms"
    - "caffe convolutional architecture for fast feature embedding"
    - "the missing piece in complex analytics low latency scalable model management and serving with velox"

Connected through hyperedge HE-7 (induced by source paper "in rdbms hardware acceleration of advanced analytics"):
  with members:
    - "distributed deep learning using synchronous stochastic gradient descent"
    - "eie efficient inference engine on compressed deep neural network"
    - "dimmwitted a study of main memory statistical analytics"
    - "towards a unified architecture for in rdbms analytics"
    - "scalable probabilistic databases with factor graphs and mcmc"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-8 (induced by source paper "unifying data model and hybrid parallelism in deep learning via tensor tiling"):
  with members:
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "very deep convolutional networks for large scale image recognition"
    - "one weird trick for parallelizing convolutional neural networks"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)