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: "structured learning modulo theories"
  Abstract: Modelling problems containing a mixture of Boolean and numerical variables is a long-standing interest of Artificial Intelligence. However, performing inference and learning in hybrid domains is a particularly daunting task. The ability to model this kind of domains is crucial in "learning to design" tasks, that is, learning applications where the goal is to learn from examples how to perform automatic {\em de novo} design of novel objects. In this paper we present Structured Learning Modulo Theories, a max-margin approach for learning in hybrid domains based on Satisfiability Modulo Theories, which allows to combine Boolean reasoning and optimization over continuous linear arithmetical constraints. The main idea is to leverage a state-of-the-art generalized Satisfiability Modulo Theory solver for implementing the inference and separation oracles of Structured Output SVMs. We validate our method on artificial and real world scenarios.

Connected through hyperedge HE-1 (induced by source paper "from minizinc to optimization modulo theories and back extended version"):
  with members:
    - "multi objective reasoning with constrained goal models"
    - "simulation based computation of certificates for safety of dynamical systems"
    - "on optimization modulo theories maxsmt and sorting networks"
    - "optimization modulo theories with linear rational costs"
    - "pushing the envelope of optimization modulo theories with linear arithmetic cost functions"
    - "optimization in smt with la q cost functions"

Connected through hyperedge HE-2 (induced by source paper "constrained counting and sampling bridging the gap between theory and practice"):
  with members:
    - "approximate probabilistic inference via word level counting"
    - "distribution aware sampling and weighted model counting for sat"
    - "balancing scalability and uniformity in sat witness generator"
    - "a scalable approximate model counter"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "a scalable and nearly uniform generator of sat witnesses"
    - "optimization with parity constraints from binary codes to discrete integration"
    - "automated feedback generation for introductory programming assignments"

Connected through hyperedge HE-3 (induced by source paper "on optimization modulo theories maxsmt and sorting networks"):
  with members:
    - "multi objective reasoning with constrained goal models"
    - "requirements evolution and evolution requirements with constrained goal models"
    - "optimization modulo theories with linear rational costs"
    - "pushing the envelope of optimization modulo theories with linear arithmetic cost functions"
    - "optimization in smt with la q cost functions"

Connected through hyperedge HE-4 (induced by source paper "approximate probabilistic inference via word level counting"):
  with members:
    - "probabilistic inference modulo theories"
    - "distribution aware sampling and weighted model counting for sat"
    - "sampling techniques for boolean satisfiability"
    - "approximate counting in smt and value estimation for probabilistic programs"
    - "a scalable approximate model counter"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"

Connected through hyperedge HE-5 (induced by source paper "learning modulo theories for preference elicitation in hybrid domains"):
  with members:
    - "minimax regret based elicitation of generalized additive utilities"

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)