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: "pipegen data pipe generator for hybrid analytics"
  Abstract: We develop a tool called PipeGen for efficient data transfer between database management systems (DBMSs). PipeGen targets data analytics workloads on shared-nothing engines. It supports scenarios where users seek to perform different parts of an analysis in different DBMSs or want to combine and analyze data stored in different systems. The systems may be colocated in the same cluster or may be in different clusters. To achieve high performance, PipeGen leverages the ability of all DBMSs to export, possibly in parallel, data into a common data format, such as CSV or JSON. It automatically extends these import and export functions with efficient binary data transfer capabilities that avoid materializing the transmitted data on the file system. We implement a prototype of PipeGen and evaluate it by automatically generating data pipes between five different DBMSs. Our experiments show that PipeGen delivers speedups up to 3.8x compared with manually exporting and importing data across systems using CSV.

Connected through hyperedge HE-1 (induced by source paper "polystore accelerated polystore system for heterogeneous workloads"):
  with members:
    - "relay a new ir for machine learning frameworks"
    - "rheemix in the data jungle a cross platform query optimizer"
    - "in rdbms hardware acceleration of advanced analytics"
    - "practical design space exploration"
    - "in datacenter performance analysis of a tensor processing unit"
    - "empowering in memory relational database engines with native graph processing"
    - "algorithmic performance accuracy trade off in 3d vision applications using hypermapper"
    - "the bigdawg polystore system and architecture"

Connected through hyperedge HE-2 (induced by source paper "building your cross platform application with rheem"):
  with members:
    - "rheemix in the data jungle a cross platform query optimizer"
    - "a cost based optimizer for gradient descent optimization"
    - "tensorflow a system for large scale machine learning"
    - "how to use big data technologies to optimize operations in upstream petroleum industry"

Connected through hyperedge HE-3 (induced by source paper "rheemix in the data jungle a cross platform query optimizer"):
  with members:
    - "a cost based optimizer for gradient descent optimization"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-4 (induced by source paper "distributed triangle counting in the graphulo matrix math library"):
  with members:
    - "laradb a minimalist kernel for linear and relational algebra computation"
    - "static graph challenge subgraph isomorphism"
    - "benchmarking the graphulo processing framework"
    - "hypergraph partitioning for sparse matrix matrix multiplication"
    - "the bigdawg polystore system and architecture"
    - "exploiting multiple levels of parallelism in sparse matrix matrix multiplication"
    - "from nosql accumulo to newsql graphulo design and utility of graph algorithms inside a bigtable database"
    - "using a power law distribution to describe big data"

Connected through hyperedge HE-5 (induced by source paper "from nosql accumulo to newsql graphulo design and utility of graph algorithms inside a bigtable database"):
  with members:
    - "using a power law distribution to describe big data"
    - "associative arrays unified mathematics for spreadsheets databases matrices and graphs"
    - "graphs matrices and the graphblas seven good reasons"
    - "graphulo implementation of server side sparse matrix multiply in the accumulo database"
    - "graphulo linear algebra graph kernels for nosql databases"
    - "achieving 100 000 000 database inserts per second using accumulo and d4m"
    - "d4m 2 0 schema a general purpose high performance schema for the accumulo database"
    - "truss decomposition in massive networks"

Connected through hyperedge HE-6 (induced by source paper "database backed applications in the wild how well do they work"):
  with members:
    - "program transformations for asynchronous and batched query submission"
    - "shareddb killing one thousand queries with one stone"
    - "on predictive modeling for optimizing transaction execution in parallel oltp systems"

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)