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: "a challenge set approach to evaluating machine translation"
  Abstract: Neural machine translation represents an exciting leap forward in translation quality. But what longstanding weaknesses does it resolve, and which remain? We address these questions with a challenge set approach to translation evaluation and error analysis. A challenge set consists of a small set of sentences, each hand-designed to probe a system's capacity to bridge a particular structural divergence between languages. To exemplify this approach, we present an English-French challenge set, and use it to analyze phrase-based and neural systems. The resulting analysis provides not only a more fine-grained picture of the strengths of neural systems, but also insight into which linguistic phenomena remain out of reach.

Connected through hyperedge HE-1 (induced by source paper "stance detection benchmark how robust is your stance detection"):
  with members:
    - "don t take the easy way out ensemble based methods for avoiding known dataset biases"
    - "stancy stance classification based on consistency cues"
    - "multi task deep neural networks for natural language understanding"
    - "seeing things from a different angle discovering diverse perspectives about claims"
    - "unlearn dataset bias in natural language inference by fitting the residual"
    - "combating adversarial misspellings with robust word recognition"
    - "are we modeling the task or the annotator an investigation of annotator bias in natural language understanding datasets"
    - "sampling bias in deep active classification an empirical study"

Connected through hyperedge HE-2 (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-3 (induced by source paper "non parametric adaptation for neural machine translation"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "effective parallel corpus mining using bilingual sentence embeddings"
    - "improving the transformer translation model with document level context"
    - "guiding neural machine translation with retrieved translation pieces"
    - "retrieval based neural code generation"
    - "the best of both worlds combining recent advances in neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "a comparative analysis of knowledge intensive and data intensive semantic parsers"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "a challenge set for french english machine translation"
    - "analysis methods in neural language processing a survey"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "amr dependency parsing with a typed semantic algebra"

Connected through hyperedge HE-5 (induced by source paper "context in neural machine translation a review of models and evaluations"):
  with members:
    - "attaining the unattainable reassessing claims of human parity in neural machine translation"
    - "assessing crosslingual discourse relations in machine translation"
    - "pronoun translation in english french machine translation an analysis of error types"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "marian fast neural machine translation in c"
    - "automatic reference based evaluation of pronoun translation misses the point"
    - "context aware neural machine translation learns anaphora resolution"
    - "a comparison of transformer and recurrent neural networks on multilingual neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "extract and edit an alternative to back translation for unsupervised neural machine translation"):
  with members:
    - "towards robust neural machine translation"
    - "response generation by context aware prototype editing"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "extracting parallel sentences with bidirectional recurrent neural networks to improve machine translation"
    - "phrase based neural unsupervised machine translation"
    - "unsupervised neural machine translation with weight sharing"
    - "generating sentences by editing prototypes"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "adversarial attacks against fact extraction and verification"):
  with members:
    - "team papelo transformer networks at fever"
    - "combining fact extraction and verification with neural semantic matching networks"
    - "the fact extraction and verification fever shared task"
    - "automated fact checking task formulations methods and future directions"
    - "ukp athene multi sentence textual entailment for claim verification"
    - "hypothesis only baselines in natural language inference"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"

Connected through hyperedge HE-8 (induced by source paper "semantic neural machine translation using amr"):
  with members:
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "graph to sequence learning using gated graph neural networks"
    - "a graph to sequence model for amr to text generation"
    - "amr dependency parsing with a typed semantic algebra"
    - "amr parsing as graph prediction with latent alignment"
    - "sentence state lstm for text representation"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"

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)