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: "automatically extracting action graphs from materials science synthesis procedures"
  Abstract: Computational synthesis planning approaches have achieved recent success in organic chemistry, where tabulated synthesis procedures are readily available for supervised learning. The syntheses of inorganic materials, however, exist primarily as natural language narratives contained within scientific journal articles. This synthesis information must first be extracted from the text in order to enable analogous synthesis planning methods for inorganic materials. In this work, we present a system for automatically extracting structured representations of synthesis procedures from the texts of materials science journal articles that describe explicit, experimental syntheses of inorganic compounds. We define the structured representation as a set of linked events made up of extracted scientific entities and evaluate two unsupervised approaches for extracting these structures on expert-annotated articles: a strong heuristic baseline and a generative model of procedural text. We also evaluate a variety of supervised models for extracting scientific entities. Our results provide insight into the nature of the data and directions for further work in this exciting new area of research.

Connected through hyperedge HE-1 (induced by source paper "annotating and extracting synthesis process of all solid state batteries from scientific literature"):
  with members:
    - "the materials science procedural text corpus annotating materials synthesis procedures with shallow semantic structures"
    - "scibert a pretrained language model for scientific text"
    - "scispacy fast and robust models for biomedical natural language processing"
    - "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension"
    - "deep contextualized word representations"
    - "an annotated corpus for machine reading of instructions in wet lab protocols"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "playing by the book an interactive game approach for action graph extraction from text"

Connected through hyperedge HE-2 (induced by source paper "on the limits of learning to actively learn semantic representations"):
  with members:
    - "practical obstacles to deploying active learning"
    - "allennlp a deep semantic natural language processing platform"
    - "multitask parsing across semantic representations"
    - "large scale qa srl parsing"
    - "breaking the beam search curse a study of re scoring methods and stopping criteria for neural machine translation"
    - "deep bayesian active learning for natural language processing results of a large scale empirical study"
    - "learning how to active learn a deep reinforcement learning approach"
    - "learning active learning from data"

Connected through hyperedge HE-3 (induced by source paper "the materials science procedural text corpus annotating materials synthesis procedures with shallow semantic structures"):
  with members:
    - "an annotated corpus for machine reading of instructions in wet lab protocols"
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "playing by the book an interactive game approach for action graph extraction from text"
    - "extracting biomolecular interactions using semantic parsing of biomedical text"

Connected through hyperedge HE-4 (induced by source paper "pipelines for procedural information extraction from scientific literature towards recipes using machine learning and data science"):
  with members:
    - "towards alphachem chemical synthesis planning with tree search and deep neural network policies"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "playing by the book an interactive game approach for action graph extraction from text"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "question answering as global reasoning over semantic abstractions"
    - "a survey of domain adaptation for neural machine translation"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "textworld a learning environment for text based games"
    - "building dynamic knowledge graphs from text using machine reading comprehension"
    - "deep dyna q integrating planning for task completion dialogue policy learning"
    - "learn what not to learn action elimination with deep reinforcement learning"

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)