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: "linspector multilingual probing tasks for word representations"
  Abstract: Despite an ever growing number of word representation models introduced for a large number of languages, there is a lack of a standardized technique to provide insights into what is captured by these models. Such insights would help the community to get an estimate of the downstream task performance, as well as to design more informed neural architectures, while avoiding extensive experimentation which requires substantial computational resources not all researchers have access to. A recent development in NLP is to use simple classification tasks, also called probing tasks, that test for a single linguistic feature such as part-of-speech. Existing studies mostly focus on exploring the linguistic information encoded by the continuous representations of English text. However, from a typological perspective the morphologically poor English is rather an outlier: the information encoded by the word order and function words in English is often stored on a morphological level in other languages. To address this, we introduce 15 type-level probing tasks such as case marking, possession, word length, morphological tag count and pseudoword identification for 24 languages. We present a reusable methodology for creation and evaluation of such tests in a multilingual setting. We then present experiments on several diverse multilingual word embedding models, in which we relate the probing task performance for a diverse set of languages to a range of five classic NLP tasks: POS-tagging, dependency parsing, semantic role labeling, named entity recognition and natural language inference. We find that a number of probing tests have significantly high positive correlation to the downstream tasks, especially for morphologically rich languages. We show that our tests can be used to explore word embeddings or black-box neural models for linguistic cues in a multilingual setting.

Connected through hyperedge HE-1 (induced by source paper "designing and interpreting probes with control tasks"):
  with members:
    - "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"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "why do masked neural language models still need common sense knowledge"):
  with members:
    - "commonsense knowledge mining from pretrained models"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "what does bert look at an analysis of bert s attention"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "commonsenseqa a question answering challenge targeting commonsense knowledge"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "linspector web a multilingual probing suite for word representations"):
  with members:
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "analysis methods in neural language processing a survey"
    - "understanding learning dynamics of language models with svcca"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "towards a seamless integration of word senses into downstream nlp applications"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "enhanced lstm for natural language inference"

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)