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: "modular action language"
  Abstract: The paper introduces a new modular action language, ALM, and illustrates the methodology of its use. It is based on the approach of Gelfond and Lifschitz (1993; 1998) in which a high-level action language is used as a front end for a logic programming system description. The resulting logic programming representation is used to perform various computational tasks. The methodology based on existing action languages works well for small and even medium size systems, but is not meant to deal with larger systems that require structuring of knowledge. ALM is meant to remedy this problem. Structuring of knowledge in ALM is supported by the concepts of module (a formal description of a specific piece of knowledge packaged as a unit), module hierarchy, and library, and by the division of a system description of ALM into two parts: theory and structure. A theory consists of one or more modules with a common theme, possibly organized into a module hierarchy based on a dependency relation. It contains declarations of sorts, attributes, and properties of the domain together with axioms describing them. Structures are used to describe the domain's objects. These features, together with the means for defining classes of a domain as special cases of previously defined ones, facilitate the stepwise development, testing, and readability of a knowledge base, as well as the creation of knowledge representation libraries. To appear in Theory and Practice of Logic Programming (TPLP).

Connected through hyperedge HE-1 (induced by source paper "a human centered data driven planner actor critic architecture via logic programming"):
  with members:
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"
    - "interactive learning from policy dependent human feedback"
    - "high dimensional continuous control using generalized advantage estimation"
    - "trust region policy optimization"
    - "the fast downward planning system"

Connected through hyperedge HE-2 (induced by source paper "restkb a library of commonsense knowledge about dining at a restaurant"):
  with members:
    - "an asp methodology for understanding narratives about stereotypical activities"
    - "an application of asp theories of intentions to understanding restaurant scenarios insights and narrative corpus"
    - "diagnostic reasoning with a prolog"

Connected through hyperedge HE-3 (induced by source paper "information extraction tool text2alm from narratives to action language system descriptions"):
  with members:
    - "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension"
    - "what happened leveraging verbnet to predict the effects of actions in procedural text"

Connected through hyperedge HE-4 (induced by source paper "sdrl interpretable and data efficient deep reinforcement learningleveraging symbolic planning"):
  with members:
    - "strategic object oriented reinforcement learning"
    - "fast exploration with simplified models and approximately optimistic planning in model based reinforcement learning"
    - "robot representing and reasoning with knowledge from reinforcement learning"
    - "programmatically interpretable reinforcement learning"
    - "hierarchical imitation and reinforcement learning"
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"
    - "explaining explanations an overview of interpretability of machine learning"
    - "eigenoption discovery through the deep successor representation"

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.LO(Logic in Computer Science)