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: "factors influencing the surprising instability of word embeddings"
  Abstract: Despite the recent popularity of word embedding methods, there is only a small body of work exploring the limitations of these representations. In this paper, we consider one aspect of embedding spaces, namely their stability. We show that even relatively high frequency words (100-200 occurrences) are often unstable. We provide empirical evidence for how various factors contribute to the stability of word embeddings, and we analyze the effects of stability on downstream tasks.

Connected through hyperedge HE-1 (induced by source paper "understanding the downstream instability of word embeddings"):
  with members:
    - "on the downstream performance of compressed word embeddings"
    - "the influence of down sampling strategies on svd word embedding stability"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "on the dimensionality of word embedding"
    - "attention is all you need"
    - "compressing word embeddings via deep compositional code learning"

Connected through hyperedge HE-2 (induced by source paper "probing for semantic classes diagnosing the meaning content of word embeddings"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "semantic structure and interpretability of word embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "conditional generators of words definitions"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "verb argument structure alternations in word and sentence embeddings"

Connected through hyperedge HE-3 (induced by source paper "characterizing the impact of geometric properties of word embeddings on task performance"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "second order word embeddings from nearest neighbor topological features"
    - "improving negative sampling for word representation using self embedded features"
    - "insights into analogy completion from the biomedical domain"
    - "the role of context types and dimensionality in learning word embeddings"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-4 (induced by source paper "density matching for bilingual word embedding"):
  with members:
    - "unsupervised statistical machine translation"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "universal neural machine translation for extremely low resource languages"
    - "near or far wide range zero shot cross lingual dependency parsing"
    - "learning multilingual word embeddings in latent metric space a geometric approach"
    - "non adversarial unsupervised word translation"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "on the limitations of unsupervised bilingual dictionary induction"

Connected through hyperedge HE-5 (induced by source paper "learning word embeddings with domain awareness"):
  with members:
    - "parser training with heterogeneous treebanks"
    - "on the dimensionality of word embedding"
    - "labeling gaps between words recognizing overlapping mentions with mention separators"
    - "a simple regularization based algorithm for learning cross domain word embeddings"
    - "integrating distributional lexical contrast into word embeddings for antonym synonym distinction"
    - "bag of tricks for efficient text classification"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "a framework for anomaly detection using language modeling and its applications to finance"):
  with members:
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "learning to select data for transfer learning with bayesian optimization"
    - "stochastic language generation in dialogue using recurrent neural networks with convolutional sentence reranking"
    - "distributed representations of words and phrases and their compositionality"
    - "from frequency to meaning vector space models of semantics"

Connected through hyperedge HE-7 (induced by source paper "understanding stability of medical concept embeddings analysis and prediction"):
  with members:
    - "medical concept embedding with time aware attention"
    - "jointly embedding entities and text with distant supervision"
    - "word2vec applied to recommendation hyperparameters matter"
    - "corpus specificity in lsa and word2vec the role of out of domain documents"
    - "gram graph based attention model for healthcare representation learning"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "metrology for ai from benchmarks to instruments"):
  with members:
    - "discovering blind spots in reinforcement learning"
    - "crowdsourcing ground truth for medical relation extraction"
    - "enriching word vectors with subword information"
    - "exposing ambiguities in a relation extraction gold standard with crowdsourcing"
    - "comprehensive and reliable crowd assessment algorithms"
    - "efficient estimation of word representations in vector space"

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)