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: "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"
  Abstract: Despite the remarkable evolution of deep neural networks in natural language processing (NLP), their interpretability remains a challenge. Previous work largely focused on what these models learn at the representation level. We break this analysis down further and study individual dimensions (neurons) in the vector representation learned by end-to-end neural models in NLP tasks. We propose two methods: Linguistic Correlation Analysis, based on a supervised method to extract the most relevant neurons with respect to an extrinsic task, and Cross-model Correlation Analysis, an unsupervised method to extract salient neurons w.r.t. the model itself. We evaluate the effectiveness of our techniques by ablating the identified neurons and reevaluating the network's performance for two tasks: neural machine translation (NMT) and neural language modeling (NLM). We further present a comprehensive analysis of neurons with the aim to address the following questions: i) how localized or distributed are different linguistic properties in the models? ii) are certain neurons exclusive to some properties and not others? iii) is the information more or less distributed in NMT vs. NLM? and iv) how important are the neurons identified through the linguistic correlation method to the overall task? Our code is publicly available as part of the NeuroX toolkit (Dalvi et al. 2019).

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 "visual interaction with deep learning models through collaborative semantic inference"):
  with members:
    - "on human predictions with explanations and predictions of machine learning models a case study on deception detection"
    - "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"
    - "semantic image synthesis with spatially adaptive normalization"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "a tutorial on deep latent variable models of natural language"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "bottom up abstractive summarization"
    - "gan dissection visualizing and understanding generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "investigating multilingual nmt representations at scale"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "consistency by agreement in zero shot neural machine translation"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "multilingual neural machine translation with language clustering"
    - "cross lingual language model pretraining"
    - "massively multilingual neural machine translation"
    - "the missing ingredient in zero shot neural machine translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"

Connected through hyperedge HE-4 (induced by source paper "neuron interaction based representation composition for neural machine translation"):
  with members:
    - "dynamic layer aggregation for neural machine translation with routing by agreement"
    - "information aggregation for multi head attention with routing by agreement"
    - "exploiting deep representations for neural machine translation"
    - "weighted transformer network for machine translation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multi head attention with disagreement regularization"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"

Connected through hyperedge HE-5 (induced by source paper "unsupervised transfer learning via bert neuron selection"):
  with members:
    - "visualizing and measuring the geometry of bert"
    - "bert rediscovers the classical nlp pipeline"
    - "open sesame getting inside bert s linguistic knowledge"
    - "revealing the dark secrets of bert"
    - "linguistic knowledge and transferability of contextual representations"
    - "meta multi task learning for sequence modeling"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "dynamic meta embeddings for improved sentence representations"

Connected through hyperedge HE-6 (induced by source paper "sparsity emerges naturally in neural language models"):
  with members:
    - "a deep generative model of vowel formant typology"
    - "understanding learning dynamics of language models with svcca"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "learning to generate reviews and discovering sentiment"
    - "exploring sparsity in recurrent neural networks"
    - "opening the black box of deep neural networks via information"
    - "spine sparse interpretable neural embeddings"
    - "representation of linguistic form and function in recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "lstm networks can perform dynamic counting"):
  with members:
    - "context free transductions with neural stacks"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "on evaluating the generalization of lstm models in formal languages"
    - "evaluating the ability of lstms to learn context free grammars"
    - "learning operations on a stack with neural turing machines"
    - "learning to transduce with unbounded memory"
    - "inferring algorithmic patterns with stack augmented recurrent nets"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "on the realization of compositionality in neural networks"):
  with members:
    - "transcoding compositionally using attention to find more generalizable solutions"
    - "learning compositionally through attentive guidance"
    - "memorize or generalize searching for a compositional rnn in a haystack"
    - "a unified approach to interpreting model predictions"
    - "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure"
    - "visualizing and understanding recurrent networks"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

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)