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: "grounding the lexical semantics of verbs in visual perception using force dynamics and event logic"
  Abstract: This paper presents an implemented system for recognizing the occurrence of events described by simple spatial-motion verbs in short image sequences. The semantics of these verbs is specified with event-logic expressions that describe changes in the state of force-dynamic relations between the participants of the event. An efficient finite representation is introduced for the infinite sets of intervals that occur when describing liquid and semi-liquid events. Additionally, an efficient procedure using this representation is presented for inferring occurrences of compound events, described with event-logic expressions, from occurrences of primitive events. Using force dynamics and event logic to specify the lexical semantics of events allows the system to be more robust than prior systems based on motion profile.

Connected through hyperedge HE-1 (induced by source paper "interactive grounded language acquisition and generalization in a 2d world"):
  with members:
    - "modulating early visual processing by language"
    - "natural language does not emerge naturally in multi agent dialog"
    - "learning to reason end to end module networks for visual question answering"
    - "zero shot task generalization with multi task deep reinforcement learning"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "mapping instructions and visual observations to actions with reinforcement learning"
    - "grounding of textual phrases in images by reconstruction"
    - "learning to compose neural networks for question answering"

Connected through hyperedge HE-2 (induced by source paper "robots learning to say no prohibition and rejective mechanisms in acquisition of linguistic negation"):
  with members:
    - "symbol conversational and societal grounding with a toy robot"
    - "the symbol grounding problem"

Connected through hyperedge HE-3 (induced by source paper "localizing moments in video with natural language"):
  with members:
    - "unsupervised visual linguistic reference resolution in instructional videos"
    - "weakly supervised learning of visual relations"
    - "detecting visual relationships with deep relational networks"
    - "tgif qa toward spatio temporal reasoning in visual question answering"
    - "tall temporal activity localization via language query"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "modeling relationships in referential expressions with compositional modular networks"
    - "learning language visual embedding for movie understanding with natural language"

Connected through hyperedge HE-4 (induced by source paper "probabilistic complex event recognition a survey"):
  with members:
    - "going deeper into action recognition a survey"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "a probabilistic logic programming event calculus"
    - "lifted relational neural networks"
    - "complex events recognition under uncertainty in a sensor network"
    - "relational bayesian networks"
    - "location based reasoning about complex multi agent behavior"
    - "a model for reasoning with uncertain rules in event composition systems"

Connected through hyperedge HE-5 (induced by source paper "grounded language learning in a simulated 3d world"):
  with members:
    - "a deep compositional framework for human like language acquisition in virtual environment"
    - "learning language games through interaction"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"
    - "a roadmap towards machine intelligence"
    - "language understanding for text based games using deep reinforcement learning"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"

Connected through hyperedge HE-6 (induced by source paper "sensorimotor input as a language generalisation tool a neurorobotics model for generation and generalisation of noun verb combinations with sensorimotor inputs"):
  with members:
    - "interactive natural language acquisition in a multi modal recurrent neural architecture"
    - "reasoning about pragmatics with neural listeners and speakers"
    - "a cognitive neural architecture able to learn and communicate through natural language"
    - "controlling recurrent neural networks by conceptors"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "show and tell a neural image caption generator"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-7 (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"
    - "generating simulations of motion events from verbal descriptions"
    - "text to 3d scene generation with rich lexical grounding"

Connected through hyperedge HE-8 (induced by source paper "voxml a visualization modeling language"):
  with members:
    - "generating simulations of motion events from verbal descriptions"
    - "text to 3d scene generation with rich lexical grounding"
    - "international standard for a linguistic annotation framework"

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)