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: "ringo interactive graph analytics on big memory machines"
  Abstract: We present Ringo, a system for analysis of large graphs. Graphs provide a way to represent and analyze systems of interacting objects (people, proteins, webpages) with edges between the objects denoting interactions (friendships, physical interactions, links). Mining graphs provides valuable insights about individual objects as well as the relationships among them.   In building Ringo, we take advantage of the fact that machines with large memory and many cores are widely available and also relatively affordable. This allows us to build an easy-to-use interactive high-performance graph analytics system. Graphs also need to be built from input data, which often resides in the form of relational tables. Thus, Ringo provides rich functionality for manipulating raw input data tables into various kinds of graphs. Furthermore, Ringo also provides over 200 graph analytics functions that can then be applied to constructed graphs.   We show that a single big-memory machine provides a very attractive platform for performing analytics on all but the largest graphs as it offers excellent performance and ease of use as compared to alternative approaches. With Ringo, we also demonstrate how to integrate graph analytics with an iterative process of trial-and-error data exploration and rapid experimentation, common in data mining workloads.

Connected through hyperedge HE-1 (induced by source paper "adaptive low level storage of very large knowledge graphs"):
  with members:
    - "a survey of rdf data management systems"
    - "snap a general purpose network analysis and graph mining library"
    - "kognac efficient encoding of large knowledge graphs"
    - "taming subgraph isomorphism for rdf query processing"
    - "s2rdf rdf querying with sparql on spark"
    - "processing sparql queries over distributed rdf graphs"

Connected through hyperedge HE-2 (induced by source paper "a graphblas approach for subgraph counting"):
  with members:
    - "design principles for sparse matrix multiplication on the gpu"
    - "high performance massive subgraph counting using pipelined adaptive group communication"
    - "accelerating pagerank using partition centric processing"
    - "subgraph counting color coding beyond trees"
    - "the reverse cuthill mckee algorithm in distributed memory"
    - "mathematical foundations of the graphblas"
    - "from nosql accumulo to newsql graphulo design and utility of graph algorithms inside a bigtable database"
    - "graphs matrices and the graphblas seven good reasons"

Connected through hyperedge HE-3 (induced by source paper "distributed graphs in search of fast low latency resource efficient semantics rich big data processing"):
  with members:
    - "high level programming abstractions for distributed graph processing"
    - "the ubiquity of large graphs and surprising challenges of graph processing"

Connected through hyperedge HE-4 (induced by source paper "rept a streaming algorithm of approximating global and local triangle counts in parallel"):
  with members:
    - "on sampling from massive graph streams"
    - "nxgraph an efficient graph processing system on a single machine"
    - "the input output complexity of triangle enumeration"
    - "graph sample and hold a framework for big graph analytics"
    - "wedge sampling for computing clustering coefficients and triangle counts on large graphs"
    - "parallel triangle counting in massive streaming graphs"
    - "a space efficient streaming algorithm for triangle counting using the birthday paradox"
    - "image labeling on a network using social network metadata for image classification"

Connected through hyperedge HE-5 (induced by source paper "extracting and analyzing hidden graphs from relational databases"):
  with members:
    - "snap a general purpose network analysis and graph mining library"
    - "a survey on methods and systems for graph compression"
    - "graph analytics using the vertica relational database"
    - "pregelix big ger graph analytics on a dataflow engine"
    - "distributed graphlab a framework for machine learning in the cloud"

Connected through hyperedge HE-6 (induced by source paper "empowering in memory relational database engines with native graph processing"):
  with members:
    - "graph analytics using the vertica relational database"
    - "relational approach for shortest path discovery over large graphs"

Connected through hyperedge HE-7 (induced by source paper "snap a general purpose network analysis and graph mining library"):
  with members:
    - "personalized pagerank estimation and search a bidirectional approach"
    - "network lasso clustering and optimization in large graphs"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "nonparametric multi group membership model for dynamic networks"
    - "community detection in networks with node attributes"
    - "discovering social circles in ego networks"
    - "structure and dynamics of information pathways in online media"
    - "latent multi group membership graph model"

Connected through hyperedge HE-8 (induced by source paper "pfo a parallelfriendly high performance system for online query and update of nearest neighbors"):
  with members:
    - "approximate similarity search for online multimedia services on distributed cpu gpu platforms"

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)