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: "what happened leveraging verbnet to predict the effects of actions in procedural text"
  Abstract: Our goal is to answer questions about paragraphs describing processes (e.g., photosynthesis). Texts of this genre are challenging because the effects of actions are often implicit (unstated), requiring background knowledge and inference to reason about the changing world states. To supply this knowledge, we leverage VerbNet to build a rulebase (called the Semantic Lexicon) of the preconditions and effects of actions, and use it along with commonsense knowledge of persistence to answer questions about change. Our evaluation shows that our system, ProComp, significantly outperforms two strong reading comprehension (RC) baselines. Our contributions are two-fold: the Semantic Lexicon rulebase itself, and a demonstration of how a simulation-based approach to machine reading can outperform RC methods that rely on surface cues alone. #R##N#Since this work was performed, we have developed neural systems that outperform ProComp, described elsewhere (Dalvi et al., NAACL'18). However, the Semantic Lexicon remains a novel and potentially useful resource, and its integration with neural systems remains a currently unexplored opportunity for further improvements in machine reading about processes.

Connected through hyperedge HE-1 (induced by source paper "everything happens for a reason discovering the purpose of actions in procedural text"):
  with members:
    - "tracking discrete and continuous entity state for process understanding"
    - "a structured learning approach to temporal relation extraction"
    - "building dynamic knowledge graphs from text using machine reading comprehension"
    - "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension"
    - "allennlp a deep semantic natural language processing platform"
    - "reasoning about actions and state changes by injecting commonsense knowledge"
    - "simulating action dynamics with neural process networks"
    - "a neural knowledge language model"

Connected through hyperedge HE-2 (induced by source paper "declarative question answering over knowledge bases containing natural language text with answer set programming"):
  with members:
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "neural natural language inference models enhanced with external knowledge"
    - "a decomposable attention model for natural language inference"
    - "bidirectional attention flow for machine comprehension"
    - "squad 100 000 questions for machine comprehension of text"
    - "a large annotated corpus for learning natural language inference"

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"
    - "modular action language"

Connected through hyperedge HE-4 (induced by source paper "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension"):
  with members:
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "simulating action dynamics with neural process networks"
    - "adversarial examples for evaluating reading comprehension systems"
    - "simple and effective multi paragraph reading comprehension"
    - "constructing datasets for multi hop reading comprehension across documents"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "squad 100 000 questions for machine comprehension of text"
    - "tracking the world state with recurrent entity networks"

Connected through hyperedge HE-5 (induced by source paper "reasoning about actions and state changes by injecting commonsense knowledge"):
  with members:
    - "modeling naive psychology of characters in simple commonsense stories"
    - "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension"
    - "lexically constrained decoding for sequence generation using grid beam search"
    - "simulating action dynamics with neural process networks"
    - "a syntactic neural model for general purpose code generation"
    - "sequence to sequence learning as beam search optimization"
    - "tracking the world state with recurrent entity networks"
    - "dialog state tracking a machine reading approach using memory network"

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)