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: "automatically leveraging mapreduce frameworks for data intensive applications"
  Abstract: MapReduce is a popular programming paradigm for developing large-scale, data-intensive computation. Many frameworks that implement this paradigm have recently been developed. To leverage these frameworks, however, developers must become familiar with their APIs and rewrite existing code. We present Casper, a new tool that automatically translates sequential Java programs into the MapReduce paradigm. Casper identifies potential code fragments to rewrite and translates them in two steps: (1) Casper uses program synthesis to search for a program summary (i.e., a functional specification) of each code fragment. The summary is expressed using a high-level intermediate language resembling the MapReduce paradigm and verified to be semantically equivalent to the original using a theorem prover. (2) Casper generates executable code from the summary, using either the Hadoop, Spark, or Flink API. We evaluated Casper by automatically converting real-world, sequential Java benchmarks to MapReduce. The resulting benchmarks perform up to 48.2x faster compared to the original.

Connected through hyperedge HE-1 (induced by source paper "extending relational query processing with ml inference"):
  with members:
    - "willump a statistically aware end to end optimizer for machine learning inference"
    - "machine learning at microsoft with ml net"
    - "cloudy with high chance of dbms a 10 year prediction for enterprise grade ml"
    - "declarative recursive computation on an rdbms or why you should use a database for distributed machine learning"
    - "pretzel opening the black box of machine learning prediction serving systems"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "laradb a minimalist kernel for linear and relational algebra computation"
    - "optimization of imperative programs in a relational database"

Connected through hyperedge HE-2 (induced by source paper "using e graphs for cad parameter inference"):
  with members:
    - "csgnet neural shape parser for constructive solid geometry"
    - "learning to infer graphics programs from hand drawn images"
    - "programmatic and direct manipulation together at last"
    - "program synthesis from polymorphic refinement types"
    - "equality saturation a new approach to optimization"

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"
    - "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"
    - "shark sql and rich analytics at scale"

Connected through hyperedge HE-4 (induced by source paper "active inductive logic programming for code search"):
  with members:
    - "sourcerercc scaling code clone detection to big code"
    - "learning syntactic program transformations from examples"
    - "leveraging parallel data processing frameworks with verified lifting"
    - "learning by transduction"

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)