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: "word representation models for morphologically rich languages in neural machine translation"
  Abstract: Dealing with the complex word forms in morphologically rich languages is an open problem in language processing, and is particularly important in translation. In contrast to most modern neural systems of translation, which discard the identity for rare words, in this paper we propose several architectures for learning word representations from character and morpheme level word decompositions. We incorporate these representations in a novel machine translation model which jointly learns word alignments and translations via a hard attention mechanism. Evaluating on translating from several morphologically rich languages into English, we show consistent improvements over strong baseline methods, of between 1 and 1.5 BLEU points.

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 "cmu 01 at the sigmorphon 2019 shared task on crosslinguality and context in morphology"):
  with members:
    - "a simple joint model for improved contextual neural lemmatization"
    - "75 languages 1 model parsing universal dependencies universally"
    - "modeling composite labels for neural morphological tagging"
    - "neural factor graph models for cross lingual morphological tagging"
    - "marrying universal dependencies and universal morphology"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "improving named entity recognition by jointly learning to disambiguate morphological tags"
    - "contextual parameter generation for universal neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "stem driven language models for morphologically rich languages"):
  with members:
    - "morphological segmentation inside out"
    - "compositional representation of morphologically rich input for neural machine translation"
    - "from characters to words to in between do we capture morphology"
    - "character word lstm language models"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "a joint model for word embedding and word morphology"
    - "generalizing and hybridizing count based and neural language models"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-4 (induced by source paper "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"):
  with members:
    - "revisiting the importance of individual units in cnns via ablation"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "identifying and controlling important neurons in neural machine translation"
    - "learning to generate reviews and discovering sentiment"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "neurox a toolkit for analyzing individual neurons in neural networks"
    - "attention is all you need"
    - "gate activation signal analysis for gated recurrent neural networks and its correlation with phoneme boundaries"

Connected through hyperedge HE-5 (induced by source paper "empirical evaluation of character based model on neural named entity recognition in indonesian conversational texts"):
  with members:
    - "from characters to words to in between do we capture morphology"
    - "semi supervised multitask learning for sequence labeling"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "fully character level neural machine translation without explicit segmentation"
    - "neural architectures for named entity recognition"
    - "attending to characters in neural sequence labeling models"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "improved transition based parsing by modeling characters instead of words with lstms"

Connected through hyperedge HE-6 (induced by source paper "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"):
  with members:
    - "analyzing hidden representations in end to end automatic speech recognition systems"
    - "gate activation signal analysis for gated recurrent neural networks and its correlation with phoneme boundaries"
    - "encoding of phonology in a recurrent neural model of grounded speech"
    - "the parallel meaning bank towards a multilingual corpus of translations annotated with compositional meaning representations"
    - "semantic tagging with deep residual networks"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "representation of linguistic form and function in recurrent neural networks"
    - "assessing the ability of lstms to learn syntax sensitive dependencies"

Connected through hyperedge HE-7 (induced by source paper "neural factor graph models for cross lingual morphological tagging"):
  with members:
    - "morphological segmentation inside out"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "cross lingual morphological tagging for low resource languages"
    - "neural morphological tagging from characters for morphologically rich languages"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "bidirectional lstm crf models for sequence tagging"
    - "loopy belief propagation for approximate inference an empirical study"

Connected through hyperedge HE-8 (induced by source paper "synthetic and natural noise both break neural machine translation"):
  with members:
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deep text classification can be fooled"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "towards crafting text adversarial samples"
    - "challenging language dependent segmentation for arabic an application to machine translation and part of speech tagging"
    - "what do neural machine translation models learn about morphology"
    - "how robust are character based word embeddings in tagging and mt against wrod scramlbing or randdm nouse"
    - "crafting adversarial input sequences for recurrent neural networks"

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.NE(Neural and Evolutionary Computing)