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: "the role of context types and dimensionality in learning word embeddings"
  Abstract: We provide the first extensive evaluation of how using different types of context to learn skip-gram word embeddings affects performance on a wide range of intrinsic and extrinsic NLP tasks. Our results suggest that while intrinsic tasks tend to exhibit a clear preference to particular types of contexts and higher dimensionality, more careful tuning is required for finding the optimal settings for most of the extrinsic tasks that we considered. Furthermore, for these extrinsic tasks, we find that once the benefit from increasing the embedding dimensionality is mostly exhausted, simple concatenation of word embeddings, learned with different context types, can yield further performance gains. As an additional contribution, we propose a new variant of the skip-gram model that learns word embeddings from weighted contexts of substitute words.

Connected through hyperedge HE-1 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity"):
  with members:
    - "multilingual alignment of contextual word representations"
    - "emerging cross lingual structure in pretrained language models"
    - "an empirical study on post processing methods for word embeddings"
    - "informing unsupervised pretraining with external linguistic knowledge"
    - "on the robustness of unsupervised and semi supervised cross lingual word embedding learning"
    - "word similarity datasets for thai construction and evaluation"
    - "paws paraphrase adversaries from word scrambling"
    - "cross lingual ability of multilingual bert an empirical study"

Connected through hyperedge HE-2 (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"
    - "factors influencing the surprising instability of word embeddings"
    - "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"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-3 (induced by source paper "temporal self attention network for medical concept embedding"):
  with members:
    - "prototype propagation networks ppn for weakly supervised few shot learning on category graph"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "medical concept embedding with time aware attention"
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "integrative analysis of patient health records and neuroimages via memory based graph convolutional network"
    - "scalable and accurate deep learning for electronic health records"
    - "reinforced mnemonic reader for machine comprehension"

Connected through hyperedge HE-4 (induced by source paper "transfer learning in biomedical natural language processing an evaluation of bert and elmo on ten benchmarking datasets"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "scibert a pretrained language model for scientific text"
    - "probing biomedical embeddings from language models"
    - "ml net multi label classification of biomedical texts with deep neural networks"
    - "enhancing clinical concept extraction with contextual embeddings"
    - "collabonet collaboration of deep neural networks for biomedical named entity recognition"
    - "transformer xl attentive language models beyond a fixed length context"
    - "publicly available clinical bert embeddings"

Connected through hyperedge HE-5 (induced by source paper "local density estimation in high dimensions"):
  with members:
    - "fast cross polytope locality sensitive hashing"
    - "node2vec scalable feature learning for networks"
    - "tight lower bounds for data dependent locality sensitive hashing"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-6 (induced by source paper "second order word embeddings from nearest neighbor topological features"):
  with members:
    - "implicitly incorporating morphological information into word embedding"
    - "the interplay of semantics and morphology in word embeddings"
    - "cross lingual models of word embeddings an empirical comparison"
    - "long short term memory networks for machine reading"
    - "node2vec scalable feature learning for networks"
    - "ultradense word embeddings by orthogonal transformation"
    - "understanding neural networks through representation erasure"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-7 (induced by source paper "deriving verb predicates by clustering verbs with arguments"):
  with members:
    - "semantic word clusters using signed normalized graph cuts"
    - "automatic sarcasm detection a survey"
    - "machine reading with background knowledge"
    - "detecting sarcasm in multimodal social platforms"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "do multi sense embeddings improve natural language understanding"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "sound word2vec learning word representations grounded in sounds"):
  with members:
    - "discriminative acoustic word embeddings recurrent neural network based approaches"
    - "multi view recurrent neural acoustic word embeddings"
    - "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"
    - "visual word2vec vis w2v learning visually grounded word embeddings using abstract scenes"
    - "combining language and vision with a multimodal skip gram model"
    - "distributed representations of words and phrases and their compositionality"

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)