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: "hume human ucca based evaluation of machine translation"
  Abstract: Human evaluation of machine translation normally uses sentence-level measures such as relative ranking or adequacy scales. However, these provide no insight into possible errors, and do not scale well with sentence length. We argue for a semantics-based evaluation, which captures what meaning components are retained in the MT output, thus providing a more fine-grained analysis of translation quality, and enabling the construction and tuning of semantics-based MT. We present a novel human semantic evaluation measure, Human UCCA-based MT Evaluation (HUME), building on the UCCA semantic representation scheme. HUME covers a wider range of semantic phenomena than previous methods and does not rely on semantic annotation of the potentially garbled MT output. We experiment with four language pairs, demonstrating HUME's broad applicability, and report good inter-annotator agreement rates and correlation with human adequacy scores.

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"
    - "semantic structural evaluation for text simplification"
    - "do rnns learn human like abstract word order preferences"
    - "a challenge set for french english machine translation"
    - "automatic metric validation for grammatical error correction"

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"
    - "semantic structural evaluation for text simplification"
    - "embedding syntax and semantics of prepositions via tensor decomposition"
    - "syntactic scaffolds for semantic structures"

Connected through hyperedge HE-3 (induced by source paper "semeval 2019 task 1 cross lingual semantic parsing with ucca"):
  with members:
    - "hlt suda at semeval 2019 task 1 ucca graph parsing as constituent tree parsing"
    - "toward abstractive summarization using semantic representations"
    - "multitask parsing across semantic representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "simple and effective text simplification using semantic and neural methods"
    - "a transition based directed acyclic graph parser for ucca"
    - "a minimal span based neural constituency parser"

Connected through hyperedge HE-4 (induced by source paper "content differences in syntactic and semantic representations"):
  with members:
    - "syntactic scaffolds for semantic structures"
    - "universal dependency parsing with a general transition based dag parser"
    - "multitask parsing across semantic representations"
    - "simple and effective text simplification using semantic and neural methods"
    - "linguistically informed self attention for semantic role labeling"
    - "a transition based directed acyclic graph parser for ucca"
    - "an incremental parser for abstract meaning representation"
    - "getting more out of syntax with props"

Connected through hyperedge HE-5 (induced by source paper "maskparse deskin at semeval 2019 task 1 cross lingual ucca semantic parsing using recursive masked sequence tagging"):
  with members:
    - "robust semantic parsing with adversarial learning for domain generalization"
    - "sources of complexity in semantic frame parsing for information extraction"
    - "semantic frame parsing for information extraction the calor corpus"
    - "a transition based directed acyclic graph parser for ucca"
    - "word translation without parallel data"
    - "enriching word vectors with subword information"
    - "training very deep networks"

Connected through hyperedge HE-6 (induced by source paper "simple and effective text simplification using semantic and neural methods"):
  with members:
    - "a transition based directed acyclic graph parser for ucca"
    - "split and rephrase"
    - "opennmt open source toolkit for neural machine translation"
    - "a constrained sequence to sequence neural model for sentence simplification"
    - "effective approaches to attention based neural machine translation"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "automatic metric validation for grammatical error correction"):
  with members:
    - "nematus a toolkit for neural machine translation"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "gleu without tuning"

Connected through hyperedge HE-8 (induced by source paper "semantic structural evaluation for text simplification"):
  with members:
    - "a transition based directed acyclic graph parser for ucca"
    - "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)