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: "gmark schema driven generation of graphs and queries"
  Abstract: Massive graph data sets are pervasive in contemporary application domains. Hence, graph database systems are becoming increasingly important. In the experimental study of these systems, it is vital that the research community has shared solutions for the generation of database instances and query workloads having predictable and controllable properties. In this paper, we present the design and engineering principles of    $\mathsf {gMark}$      , a domain- and query language-independent graph instance and query workload generator. A core contribution of    $\mathsf {gMark}$       is its ability to target and control the diversity of properties of both the generated instances and the generated workloads coupled to these instances. Further novelties include support for regular path queries, a fundamental graph query paradigm, and schema-driven selectivity estimation of queries, a key feature in controlling workload chokepoints. We illustrate the flexibility and practical usability of   $\mathsf {gMark}$       by showcasing the framework's capabilities in generating high quality graphs and workloads, and its ability to encode user-defined schemas across a variety of application domains.

Connected through hyperedge HE-1 (induced by source paper "graph generators state of the art and open challenges"):
  with members:
    - "a survey on theoretical advances of community detection in networks"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "motifs in temporal networks"
    - "g core a core for future graph query languages"
    - "darwini generating realistic large scale social graphs"
    - "an introduction to temporal graphs an algorithmic perspective"

Connected through hyperedge HE-2 (induced by source paper "structural indexing for conjunctive path queries"):
  with members:
    - "the ubiquity of large graphs and surprising challenges of graph processing extended survey"
    - "survey and taxonomy of lossless graph compression and space efficient graph representations"
    - "g core a core for future graph query languages"
    - "an analytical study of large sparql query logs"
    - "foundations of modern query languages for graph databases"
    - "taming subgraph isomorphism for rdf query processing"
    - "similarity and bisimilarity notions appropriate for characterizing indistinguishability in fragments of the calculus of relations"
    - "relative expressive power of navigational querying on graphs"

Connected through hyperedge HE-3 (induced by source paper "approximate evaluation of label constrained reachability queries"):
  with members:
    - "estimating the cardinality of conjunctive queries over rdf data using graph summarisation"
    - "g core a core for future graph query languages"
    - "an analytical study of large sparql query logs"
    - "the ubiquity of large graphs and surprising challenges of graph processing"
    - "foundations of modern query languages for graph databases"
    - "graph summarization methods and applications a survey"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

Connected through hyperedge HE-4 (induced by source paper "a tool framework for tweaking features in synthetic datasets"):
  with members:
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "bigdatabench a big data benchmark suite from internet services"

Connected through hyperedge HE-5 (induced by source paper "certified graph view maintenance with regular datalog"):
  with members:
    - "foundations of modern query languages for graph databases"
    - "incremental view maintenance for deductive graph databases using generalized discrimination networks"

Connected through hyperedge HE-6 (induced by source paper "an analytical study of large sparql query logs"):
  with members:
    - "enumeration problems for regular path queries"
    - "old techniques for new join algorithms a case study in rdf processing"
    - "on the statistical analysis of practical sparql queries"
    - "flexible caching in trie joins"
    - "a trichotomy for regular simple path queries on graphs"
    - "an empirical study of real world sparql queries"
    - "semantics and complexity of sparql"
    - "hypertree decompositions and tractable queries"

Connected through hyperedge HE-7 (induced by source paper "dynamic complexity under definable changes"):
  with members:
    - "reachability and distances under multiple changes"
    - "dynamic conjunctive queries"
    - "a strategy for dynamic programs start over and muddle through"
    - "the dynamic descriptive complexity of k clique"
    - "reachability is in dynfo"
    - "on the quantifier free dynamic complexity of reachability"
    - "dbtoaster higher order delta processing for dynamic frequently fresh views"

Connected through hyperedge HE-8 (induced by source paper "towards a property graph generator for benchmarking"):
  with members:
    - "darwini generating realistic large scale social graphs"
    - "a scalable generative graph model with community structure"

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)