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: "a linear algebraic approach to datalog evaluation"
  Abstract: In this paper, we propose a fundamentally new approach to Datalog evaluation. Given a linear Datalog program DB written using N constants and binary predicates, we first translate if-and-only-if completions of clauses in DB into a set Eq(DB) of matrix equations with a non-linear operation where relations in M_DB, the least Herbrand model of DB, are encoded as adjacency matrices. We then translate Eq(DB) into another, but purely linear matrix equations tilde_Eq(DB). It is proved that the least solution of tilde_Eq(DB) in the sense of matrix ordering is converted to the least solution of Eq(DB) and the latter gives M_DB as a set of adjacency matrices. Hence computing the least solution of tilde_Eq(DB) is equivalent to computing M_DB specified by DB. For a class of tail recursive programs and for some other types of programs, our approach achieves O(N^3) time complexity irrespective of the number of variables in a clause since only matrix operations costing O(N^3) or less are used. #R##N#We conducted two experiments that compute the least Herbrand models of linear Datalog programs. The first experiment computes transitive closure of artificial data and real network data taken from the Koblenz Network Collection. The second one compared the proposed approach with the state-of-the-art symbolic systems including two Prolog systems and two ASP systems, in terms of computation time for a transitive closure program and the same generation program. In the experiment, it is observed that our linear algebraic approach runs 10^1 ~ 10^4 times faster than the symbolic systems when data is not sparse. To appear in Theory and Practice of Logic Programming (TPLP).

Connected through hyperedge HE-1 (induced by source paper "a tensorized logic programming language for large scale data"):
  with members:
    - "deepproblog neural probabilistic logic programming"
    - "learning explanatory rules from noisy data"
    - "end to end differentiable proving"
    - "tensorflow a system for large scale machine learning"
    - "adam a method for stochastic optimization"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "a semantic matching energy function for learning with multi relational data"

Connected through hyperedge HE-2 (induced by source paper "on the expressive power of query languages for matrices"):
  with members:
    - "on matrices and k relations"
    - "on the expressive power of linear algebra on graphs"
    - "laradb a minimalist kernel for linear and relational algebra computation"
    - "embedding tarskian semantics in vector spaces"
    - "towards linear algebra over normalized data"
    - "reachability is in dynfo"
    - "rank logic is dead long live rank logic"
    - "faq questions asked frequently"

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)