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: "transforming complex sentences into a semantic hierarchy"
  Abstract: We present an approach for recursively splitting and rephrasing complex English sentences into a novel semantic hierarchy of simplified sentences, with each of them presenting a more regular structure that may facilitate a wide variety of artificial intelligence tasks, such as machine translation (MT) or information extraction (IE). Using a set of hand-crafted transformation rules, input sentences are recursively transformed into a two-layered hierarchical representation in the form of core sentences and accompanying contexts that are linked via rhetorical relations. In this way, the semantic relationship of the decomposed constituents is preserved in the output, maintaining its interpretability for downstream applications. Both a thorough manual analysis and automatic evaluation across three datasets from two different domains demonstrate that the proposed syntactic simplification approach outperforms the state of the art in structural text simplification. Moreover, an extrinsic evaluation shows that when applying our framework as a preprocessing step the performance of state-of-the-art Open IE systems can be improved by up to 346% in precision and 52% in recall. To enable reproducible research, all code is provided online.

Connected through hyperedge HE-1 (induced by source paper "fact aware sentence split and rephrase with permutation invariant training"):
  with members:
    - "split and rephrase better evaluation and a stronger baseline"
    - "learning to split and rephrase from wikipedia edit history"
    - "deep attractor network for single microphone speaker separation"
    - "split and rephrase"
    - "attention is all you need"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "multi domain neural network language generation for spoken dialogue systems"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-2 (induced by source paper "identifying supporting facts for multi hop question answering with document graph networks"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "bilinear attention networks"
    - "graphene semantically linked propositions in open information extraction"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "learning to search in long documents using document structure"
    - "know what you don t know unanswerable questions for squad"
    - "relational inductive biases deep learning and graph networks"
    - "open domain question answering using early fusion of knowledge bases and text"

Connected through hyperedge HE-3 (induced by source paper "minwikisplit a sentence splitting corpus with minimal propositions"):
  with members:
    - "semantic structural evaluation for text simplification"
    - "graphene semantically linked propositions in open information extraction"
    - "bleu is not suitable for the evaluation of text simplification"
    - "neural network acceptability judgments"
    - "learning to split and rephrase from wikipedia edit history"
    - "split and rephrase"

Connected through hyperedge HE-4 (induced by source paper "dissim a discourse aware syntactic text simplification frameworkfor english and german"):
  with members:
    - "a survey on open information extraction"
    - "split and rephrase"

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)