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: "kognac efficient encoding of large knowledge graphs"
  Abstract: Many Web applications require efficient querying of large Knowledge Graphs (KGs). We propose KOGNAC, a dictionary-encoding algorithm designed to improve SPARQL querying with a judicious combination of statistical and semantic techniques. In KOGNAC, frequent terms are detected with a frequency approximation algorithm and encoded to maximise compression. Infrequent terms are semantically grouped into ontological classes and encoded to increase data locality. We evaluated KOGNAC in combination with state-of-the-art RDF engines, and observed that it significantly improves SPARQL querying on KGs with up to 1B edges.

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"
    - "taming subgraph isomorphism for rdf query processing"
    - "ringo interactive graph analytics on big memory machines"
    - "s2rdf rdf querying with sparql on spark"
    - "processing sparql queries over distributed rdf graphs"

Connected through hyperedge HE-2 (induced by source paper "survey and taxonomy of lossless graph compression and space efficient graph representations"):
  with members:
    - "grammar based graph compression"
    - "compressing networks with super nodes"
    - "graphzip dictionary based compression for mining graph streams"
    - "parallel construction of compact planar embeddings"
    - "compressed representation of dynamic binary relations with applications"
    - "a succinct data structure for self indexing ternary relations"
    - "fully dynamic de bruijn graphs"
    - "compressing graphs and indexes with recursive graph bisection"

Connected through hyperedge HE-3 (induced by source paper "compressed representation of dynamic binary relations with applications"):
  with members:
    - "compressed k2 triples for full in memory rdf engines"
    - "practical entropy compressed rank select dictionary"

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.AI(Artificial Intelligence)