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: "on the evaluation of semantic phenomena in neural machine translation using natural language inference"
  Abstract: We propose a process for investigating the extent to which sentence representations arising from neural machine translation (NMT) systems encode distinct semantic phenomena. We use these representations as features to train a natural language inference (NLI) classifier based on datasets recast from existing semantic annotations. In applying this process to a representative NMT system, we find its encoder appears most suited to supporting inferences at the syntax-semantics interface, as compared to anaphora resolution requiring world-knowledge. We conclude with a discussion on the merits and potential deficiencies of the existing process, and how it may be improved and extended as a broader framework for evaluating semantic coverage.

Connected through hyperedge HE-1 (induced by source paper "what do you learn from context probing for sentence structure in contextualized word representations"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "neural davidsonian semantic proto role labeling"
    - "assessing composition in sentence vector representations"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "allennlp a deep semantic natural language processing platform"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "probing what different nlp tasks teach machines about function word comprehension"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "on the convergence of adam and beyond"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "the natural language decathlon multitask learning as question answering"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "comprehensive supersense disambiguation of english prepositions and possessives"
    - "allennlp a deep semantic natural language processing platform"

Connected through hyperedge HE-3 (induced by source paper "understanding neural machine translation by simplification the case of encoder free models"):
  with members:
    - "adding interpretable attention to neural translation models improves word alignment"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "a call for clarity in reporting bleu scores"
    - "an analysis of attention mechanisms the case of word sense disambiguation in neural machine translation"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "what does attention in neural machine translation pay attention to"
    - "attention focusing for neural machine translation by bridging source and target embeddings"

Connected through hyperedge HE-4 (induced by source paper "rnns implicitly implement tensor product representations"):
  with members:
    - "assessing composition in sentence vector representations"
    - "learning distributed representations of symbolic structure using binding and unbinding operations"
    - "long short term memory as a dynamically computed element wise weighted sum"
    - "relational inductive biases deep learning and graph networks"
    - "learning to reason with third order tensor products"
    - "tree to tree neural networks for program translation"
    - "evaluating compositionality in sentence embeddings"
    - "senteval an evaluation toolkit for universal sentence representations"

Connected through hyperedge HE-5 (induced by source paper "lexicosyntactic inference in neural models"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "neural models of factuality"
    - "performance impact caused by hidden bias of training data for recognizing textual entailment"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "evaluating compositionality in sentence embeddings"
    - "senteval an evaluation toolkit for universal sentence representations"

Connected through hyperedge HE-6 (induced by source paper "neural davidsonian semantic proto role labeling"):
  with members:
    - "deep contextualized word representations"
    - "neural models of factuality"
    - "universal language model fine tuning for text classification"
    - "opennmt open source toolkit for neural machine translation"
    - "learned in translation contextualized word vectors"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "how transferable are neural networks in nlp applications"

Connected through hyperedge HE-7 (induced by source paper "hypothesis only baselines in natural language inference"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "performance impact caused by hidden bias of training data for recognizing textual entailment"
    - "the repeval 2017 shared task multi genre natural language inference with sentence representations"
    - "natural language inference from multiple premises"
    - "the effect of different writing tasks on linguistic style a case study of the roc story cloze task"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "crowdsourcing multiple choice science questions"
    - "vqa visual question answering"

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)