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: "understanding learning dynamics of language models with svcca"
  Abstract: Research has shown that neural models implicitly encode linguistic features, but there has been no research showing \emph{how} these encodings arise as the models are trained. We present the first study on the learning dynamics of neural language models, using a simple and flexible analysis method called Singular Vector Canonical Correlation Analysis (SVCCA), which enables us to compare learned representations across time and across models, without the need to evaluate directly on annotated data. We probe the evolution of syntactic, semantic, and topic representations and find that part-of-speech is learned earlier than topic; that recurrent layers become more similar to those of a tagger during training; and embedding layers less similar. Our results and methods could inform better learning algorithms for NLP models, possibly to incorporate linguistic information more effectively.

Connected through hyperedge HE-1 (induced by source paper "analyzing and interpreting neural networks for nlp a report on the first blackboxnlp workshop"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "indicatements that character language models learn english morpho syntactic units and regularities"
    - "what do rnn language models learn about filler gap dependencies"
    - "rule induction for global explanation of trained models"
    - "understanding convolutional neural networks for text classification"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "lisa explaining recurrent neural network judgments via layer wise semantic accumulation and example to pattern transformation"

Connected through hyperedge HE-2 (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-3 (induced by source paper "probing multilingual sentence representations with x probe"):
  with members:
    - "scalable cross lingual transfer of neural sentence embeddings"
    - "cross lingual language model pretraining"
    - "linguistic knowledge and transferability of contextual representations"
    - "xnli evaluating cross lingual sentence representations"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "rapid learning or feature reuse towards understanding the effectiveness of maml"):
  with members:
    - "similarity of neural network representations revisited"
    - "meta learning with differentiable convex optimization"
    - "meta learning representations for continual learning"
    - "few shot text classification with distributional signatures"
    - "a closer look at few shot classification"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "transfusion understanding transfer learning for medical imaging"
    - "on first order meta learning algorithms"

Connected through hyperedge HE-5 (induced by source paper "transfusion understanding transfer learning for medical imaging"):
  with members:
    - "similarity of neural network representations revisited"
    - "investigating multilingual nmt representations at scale"
    - "the bottom up evolution of representations in the transformer a study with machine translation and language modeling objectives"
    - "rethinking imagenet pre training"
    - "direct uncertainty prediction for medical second opinions"
    - "do better imagenet models transfer better"
    - "a closer look at deep learning heuristics learning rate restarts warmup and distillation"
    - "taskonomy disentangling task transfer learning"

Connected through hyperedge HE-6 (induced by source paper "designing and interpreting probes with control tasks"):
  with members:
    - "linspector multilingual probing tasks for word representations"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "probing what different nlp tasks teach machines about function word comprehension"
    - "linguistic knowledge and transferability of contextual representations"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "analyzing learned representations of a deep asr performance prediction model"
    - "assessing composition in sentence vector representations"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"

Connected through hyperedge HE-7 (induced by source paper "zero shot reading comprehension by cross lingual transfer learning with multi lingual language representation model"):
  with members:
    - "studying the inductive biases of rnns with synthetic variations of natural languages"
    - "density matching for bilingual word embedding"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "know what you don t know unanswerable questions for squad"
    - "multilingual extractive reading comprehension by runtime machine translation"
    - "drcd a chinese machine reading comprehension dataset"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

Connected through hyperedge HE-8 (induced by source paper "correlating neural and symbolic representations of language"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "symbolic inductive bias for visually grounded learning of spoken language"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "deep contextualized word representations"
    - "how agents see things on visual representations in an emergent language game"
    - "analysis methods in neural language processing a survey"
    - "limitations in learning an interpreted language with recurrent models"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)