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: "grammar based random walkers in semantic networks"
  Abstract: Semantic networks qualify the meaning of an edge relating any two vertices. Determining which vertices are most ''central'' in a semantic network is difficult because one relationship type may be deemed subjectively more important than another. For this reason, research into semantic network metrics has focused primarily on context-based rankings (i.e. user prescribed contexts). Moreover, many of the current semantic network metrics rank semantic associations (i.e. directed paths between two vertices) and not the vertices themselves. This article presents a framework for calculating semantically meaningful primary eigenvector-based metrics such as eigenvector centrality and PageRank in semantic networks using a modified version of the random walker model of Markov chain analysis. Random walkers, in the context of this article, are constrained by a grammar, where the grammar is a user-defined data structure that determines the meaning of the final vertex ranking. The ideas in this article are presented within the context of the Resource Description Framework (RDF) of the Semantic Web initiative.

Connected through hyperedge HE-1 (induced by source paper "active learning for graph embedding"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "active discriminative text representation learning"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-2 (induced by source paper "the gremlin graph traversal machine and language invited talk"):
  with members:
    - "reconciliation of rdf and property graphs"
    - "the rdf virtual machine"
    - "exposing multi relational networks to single relational network analysis algorithms"
    - "mapping semantic networks to undirected networks"

Connected through hyperedge HE-3 (induced by source paper "a novel measure of edge centrality in social networks"):
  with members:
    - "generalized louvain method for community detection in large networks"
    - "grammar based geodesics in semantic networks"

Connected through hyperedge HE-4 (induced by source paper "the rdf virtual machine"):
  with members:
    - "general purpose computing on a semantic network substrate"
    - "grammar based geodesics in semantic networks"
    - "a reflection on the structure and process of the web of data"
    - "a distributed process infrastructure for a distributed data structure"
    - "a multi relational network to support the scholarly communication process"

Connected through hyperedge HE-5 (induced by source paper "exposing multi relational networks to single relational network analysis algorithms"):
  with members:
    - "interpretations of the web of data"
    - "social decision making with multi relational networks and grammar based particle swarms"
    - "a multi relational network to support the scholarly communication process"
    - "co authorship networks in the digital library research community"

Connected through hyperedge HE-6 (induced by source paper "grammar based geodesics in semantic networks"):
  with members:
    - "exposing multi relational networks to single relational network analysis algorithms"
    - "mapping semantic networks to undirected networks"
    - "towards usage based impact metrics first results from the mesur project"
    - "social decision making with multi relational networks and grammar based particle swarms"

Connected through hyperedge HE-7 (induced by source paper "general purpose computing on a semantic network substrate"):
  with members:
    - "using rdf to model the structure and process of systems"
    - "a practical ontology for the large scale modeling of scholarly artifacts and their usage"

Connected through hyperedge HE-8 (induced by source paper "the graph traversal pattern"):
  with members:
    - "exposing multi relational networks to single relational network analysis algorithms"
    - "mapping semantic networks to undirected networks"

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)