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: "semantic structural evaluation for text simplification"
  Abstract: Current measures for evaluating text simplification systems focus on evaluating lexical text aspects, neglecting its structural aspects. In this paper we propose the first measure to address structural aspects of text simplification, called SAMSA. It leverages recent advances in semantic parsing to assess simplification quality by decomposing the input based on its semantic structure and comparing it to the output. SAMSA provides a reference-less automatic evaluation procedure, avoiding the problems that reference-based methods face due to the vast space of valid simplifications for a given sentence. Our human evaluation experiments show both SAMSA's substantial correlation with human judgments, as well as the deficiency of existing reference-based measures in evaluating structural simplification.

Connected through hyperedge HE-1 (induced by source paper "automatically extracting challenge sets for non local phenomena neural machine translation"):
  with members:
    - "star transformer"
    - "linguistic evaluation of german english machine translation using a test suite"
    - "linguistic knowledge and transferability of contextual representations"
    - "semantic neural machine translation using amr"
    - "do rnns learn human like abstract word order preferences"
    - "a challenge set for french english machine translation"
    - "automatic metric validation for grammatical error correction"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "made for each other broad coverage semantic structures meet preposition supersenses"):
  with members:
    - "semeval 2019 task 1 cross lingual semantic parsing with ucca"
    - "on the convergence of adam and beyond"
    - "hlt suda at semeval 2019 task 1 ucca graph parsing as constituent tree parsing"
    - "semantically constrained multilayer annotation the case of coreference"
    - "linguistic knowledge and transferability of contextual representations"
    - "embedding syntax and semantics of prepositions via tensor decomposition"
    - "syntactic scaffolds for semantic structures"
    - "comprehensive supersense disambiguation of english prepositions and possessives"

Connected through hyperedge HE-3 (induced by source paper "transforming complex sentences into a semantic hierarchy"):
  with members:
    - "graphene semantically linked propositions in open information extraction"
    - "bleu is not suitable for the evaluation of text simplification"
    - "simple and effective text simplification using semantic and neural methods"
    - "learning to split and rephrase from wikipedia edit history"
    - "split and rephrase"
    - "sentence simplification with deep reinforcement learning"
    - "get to the point summarization with pointer generator networks"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-4 (induced by source paper "minwikisplit a sentence splitting corpus with minimal propositions"):
  with members:
    - "transforming complex sentences into a semantic hierarchy"
    - "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-5 (induced by source paper "easse easier automatic sentence simplification evaluation"):
  with members:
    - "reference less quality estimation of text simplification systems"
    - "bleu is not suitable for the evaluation of text simplification"
    - "a call for clarity in reporting bleu scores"
    - "a transition based directed acyclic graph parser for ucca"
    - "attention is all you need"
    - "sentence simplification 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)