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: "generating simulations of motion events from verbal descriptions"
  Abstract: In this paper, we describe a computational model for motion events in natural language that maps from linguistic expressions, through a dynamic event interpretation, into three-dimensional temporal simulations in a model. Starting with the model from (Pustejovsky and Moszkowicz, 2011), we analyze motion events using temporally-traced Labelled Transition Systems. We model the distinction between path- and manner-motion in an operational semantics, and further distinguish different types of manner-of-motion verbs in terms of the mereo-topological relations that hold throughout the process of movement. From these representations, we generate minimal models, which are realized as three-dimensional simulations in software developed with the game engine, Unity. The generated simulations act as a conceptual "debugger" for the semantics of different motion verbs: that is, by testing for consistency and informativeness in the model, simulations expose the presuppositions associated with linguistic expressions and their compositions. Because the model generation component is still incomplete, this paper focuses on an implementation which maps directly from linguistic interpretations into the Unity code snippets that create the simulations.

Connected through hyperedge HE-1 (induced by source paper "a formal analysis of multimodal referring strategies under common ground"):
  with members:
    - "grounding language attributes to objects using bayesian eigenobjects"
    - "generating justifications for norm related agent decisions"
    - "multimodal attention branch network for perspective free sentence generation"
    - "multimodal continuation style architectures for human robot interaction"
    - "interactive visual grounding of referring expressions for human robot interaction"
    - "neuralreg an end to end approach to referring expression generation"
    - "multimodal grounding for language processing"
    - "a visual attention grounding neural model for multimodal machine translation"

Connected through hyperedge HE-2 (induced by source paper "multimodal semantic simulations of linguistically underspecified motion events"):
  with members:
    - "fine grained event learning of human object interaction with lstm crf"
    - "voxml a visualization modeling language"
    - "ecat event capture annotation tool"
    - "globally normalized transition based neural networks"
    - "text to 3d scene generation with rich lexical grounding"
    - "grounding the lexical semantics of verbs in visual perception using force dynamics and event logic"

Connected through hyperedge HE-3 (induced by source paper "voxml a visualization modeling language"):
  with members:
    - "text to 3d scene generation with rich lexical grounding"
    - "international standard for a linguistic annotation framework"
    - "grounding the lexical semantics of verbs in visual perception using force dynamics and event logic"

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.CL(Computation and Language)