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: "the parallel meaning bank towards a multilingual corpus of translations annotated with compositional meaning representations"
  Abstract: The Parallel Meaning Bank is a corpus of translations annotated with shared, formal meaning representations comprising over 11 million words divided over four languages (English, German, Italian, and Dutch). Our approach is based on cross-lingual projection: automatically produced (and manually corrected) semantic annotations for English sentences are mapped onto their word-aligned translations, assuming that the translations are meaning-preserving. The semantic annotation consists of five main steps: (i) segmentation of the text in sentences and lexical items; (ii) syntactic parsing with Combinatory Categorial Grammar; (iii) universal semantic tagging; (iv) symbolization; and (v) compositional semantic analysis based on Discourse Representation Theory. These steps are performed using statistical models trained in a semi-supervised manner. The employed annotation models are all language-neutral. Our first results are promising.

Connected through hyperedge HE-1 (induced by source paper "on the linguistic representational power of neural machine translation models"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "the emergence of number and syntax units in lstm language models"
    - "a hybrid morpheme word representation for machine translation of morphologically rich languages"
    - "linguistic knowledge and transferability of contextual representations"
    - "the missing ingredient in zero shot neural machine translation"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "bpe and charcnns for translation of morphology a cross lingual comparison and analysis"

Connected through hyperedge HE-2 (induced by source paper "linguistic knowledge and transferability of contextual representations"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "what can linguistics and deep learning contribute to each other"
    - "what do rnn language models learn about filler gap dependencies"
    - "what s going on in neural constituency parsers an analysis"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "why self attention a targeted evaluation of neural machine translation architectures"

Connected through hyperedge HE-3 (induced by source paper "a survey of cross lingual features for zero shot cross lingual semantic parsing"):
  with members:
    - "polyglot semantic role labeling"
    - "cross lingual semantic parsing"
    - "exploring neural methods for parsing discourse representation structures"
    - "evaluating scoped meaning representations"
    - "coarse to fine decoding for neural semantic parsing"
    - "neural networks for cross lingual negation scope detection"
    - "universal semantic parsing"
    - "cross lingual abstract meaning representation parsing"

Connected through hyperedge HE-4 (induced by source paper "can neural networks understand monotonicity reasoning"):
  with members:
    - "help a dataset for identifying shortcomings of neural models in monotonicity reasoning"
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "hypothesis only baselines in natural language inference"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "can neural networks understand logical entailment"
    - "performance impact caused by hidden bias of training data for recognizing textual entailment"

Connected through hyperedge HE-5 (induced by source paper "help a dataset for identifying shortcomings of neural models in monotonicity reasoning"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "hypothesis only baselines in natural language inference"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "stress testing neural models of natural language inference with multiply quantified sentences"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "performance impact caused by hidden bias of training data for recognizing textual entailment"

Connected through hyperedge HE-6 (induced by source paper "semantic graph parsing with recurrent neural network dag grammars"):
  with members:
    - "exploring neural methods for parsing discourse representation structures"
    - "learning deep generative models of graphs"
    - "sequence to action end to end semantic graph generation for semantic parsing"
    - "amr dependency parsing with a typed semantic algebra"
    - "amr parsing as graph prediction with latent alignment"
    - "towards universal semantic tagging"
    - "robust incremental neural semantic graph parsing"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-7 (induced by source paper "natural language semantics with pictures some language vision datasets and potential uses for computational semantics"):
  with members:
    - "using syntax to ground referring expressions in natural images"
    - "yolov3 an incremental improvement"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mask r cnn"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "modeling context in referring expressions"
    - "generation and comprehension of unambiguous object descriptions"

Connected through hyperedge HE-8 (induced by source paper "transductive auxiliary task self training for neural multi task models"):
  with members:
    - "choosing transfer languages for cross lingual learning"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural amr sequence to sequence models for parsing and generation"
    - "one model to rule them all multitask and multilingual modelling for lexical analysis"
    - "learning to select data for transfer learning with bayesian optimization"
    - "semantic tagging with deep residual networks"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"

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)