human: Given a node-centered hypergraph: <hypergraph>, where nodes represent authors and hyperedges represent groups of authors who co-authored the same paper.

Details of the hypergraph:
Center node: "Solving SAT and SAT Modulo Theories: From an abstract Davis--Putnam--Logemann--Loveland procedure to DPLL( T )"
  Abstract: We first introduce  Abstract DPLL , a rule-based formulation of the Davis--Putnam--Logemann--Loveland (DPLL) procedure for propositional satisfiability. This abstract framework allows one to cleanly express practical DPLL algorithms and to formally reason about them in a simple way. Its properties, such as soundness, completeness or termination, immediately carry over to the modern DPLL implementations with features such as backjumping or clause learning.We then extend the framework to Satisfiability Modulo background Theories (SMT) and use it to model several variants of the so-called  lazy approach  for SMT. In particular, we use it to introduce a few variants of a new, efficient and modular approach for SMT based on a general DPLL( X ) engine, whose parameter  X  can be instantiated with a specialized solver  Solver   T   for a given theory  T , thus producing a DPLL( T ) system. We describe the high-level design of DPLL( X ) and its cooperation with  Solver   T  , discuss the role of  theory propagation , and describe different DPLL( T ) strategies for some theories arising in industrial applications.Our extensive experimental evidence, summarized in this article, shows that DPLL( T ) systems can significantly outperform the other state-of-the-art tools, frequently even in orders of magnitude, and have better scaling properties.

Connected through hyperedge HE-1 (induced by source paper "Potassco: The Potsdam Answer Set Solving Collection"):
  with members:
    - "A system for the use of answer set programming in reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "Lazy explanations for constraint propagators"):
  with members:
    - "Efficient conflict driven learning in a boolean satisfiability solver"
    - "Learning When to Use Lazy Learning in Constraint Solving"

Connected through hyperedge HE-3 (induced by source paper "Volume Computation for Boolean Combination of Linear Arithmetic Constraints"):
  with members:
    (none)

Connected through hyperedge HE-4 (induced by source paper "Efficient conflict driven learning in a boolean satisfiability solver"):
  with members:
    - "Analysis of Search Based Algorithms for Satisfiability of Quantified Boolean Formulas Arising from Circuit State Space Diameter Problems."
    - "Lazy explanations for constraint propagators"
    - "A new approach to the maximum flow problem"
    - "Efficient Solving of Large Non-linear Arithmetic Constraint Systems with Complex Boolean Structure"

Question: Please tell me which research area the center author belongs to. The 4 classes are: Computer science, Mathematics, Engineering, Economics, Psychology, Business. Directly output the class name.

gpt: Mathematics