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: "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"
  Abstract: Maybe the single most important goal of representation learning is making subsequent learning faster. Surprisingly, this fact is not well reflected in the way embeddings are evaluated. In addition, recent practice in word embeddings points towards importance of learning specialized representations. We argue that focus of word representation evaluation should reflect those trends and shift towards evaluating what useful information is easily accessible. Specifically, we propose that evaluation should focus on data efficiency and simple supervised tasks, where the amount of available data is varied and scores of a supervised model are reported for each subset (as commonly done in transfer learning). #R##N#In order to illustrate significance of such analysis, a comprehensive evaluation of selected word embeddings is presented. Proposed approach yields a more complete picture and brings new insight into performance characteristics, for instance information about word similarity or analogy tends to be non--linearly encoded in the embedding space, which questions the cosine-based, unsupervised, evaluation methods. All results and analysis scripts are available online.

Connected through hyperedge HE-1 (induced by source paper "gating mechanisms for combining character and word level word representations an empirical study"):
  with members:
    - "no training required exploring random encoders for sentence classification"
    - "learning universal sentence representations with mean max attention autoencoder"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "learning general purpose distributed sentence representations via large scale multi task 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"
    - "factors influencing the surprising instability of word embeddings"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"

Connected through hyperedge HE-3 (induced by source paper "costra 1 0 a dataset of complex sentence transformations"):
  with members:
    - "pitfalls in the evaluation of sentence embeddings"
    - "derivational morphological relations in word embeddings"
    - "no training required exploring random encoders for sentence classification"
    - "examining structure of word embeddings with pca"
    - "in search for linear relations in sentence embedding spaces"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"

Connected through hyperedge HE-4 (induced by source paper "an empirical study on post processing methods for word embeddings"):
  with members:
    - "similarity of neural network representations revisited"
    - "hyperbolic attention networks"
    - "embedding text in hyperbolic spaces"
    - "on the dimensionality of word embedding"
    - "all but the top simple and effective postprocessing for word representations"
    - "poincar e embeddings for learning hierarchical representations"
    - "word translation without parallel data"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-5 (induced by source paper "retro relation retrofitting for in database machine learning on textual data"):
  with members:
    - "termite a system for tunneling through heterogeneous data"
    - "idel in database entity linking with neural embeddings"
    - "graph embedding techniques applications and performance a survey"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "enriching word vectors with subword information"
    - "counter fitting word vectors to linguistic constraints"
    - "an ensemble method to produce high quality word embeddings"
    - "sparse overcomplete word vector representations"

Connected through hyperedge HE-6 (induced by source paper "integrating lexical knowledge in word embeddings using sprinkling and retrofitting"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "incorporating syntactic and semantic information in word embeddings using graph convolutional networks"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "counter fitting word vectors to linguistic constraints"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"

Connected through hyperedge HE-7 (induced by source paper "word similarity datasets for thai construction and evaluation"):
  with members:
    - "relation extraction datasets in the digital humanities domain and their evaluation with word embeddings"
    - "construction of a japanese word similarity dataset"
    - "bpemb tokenization free pre trained subword embeddings in 275 languages"
    - "evaluation of croatian word embeddings"
    - "the effects of data size and frequency range on distributional semantic models"
    - "enriching word vectors with subword information"
    - "problems with evaluation of word embeddings using word similarity tasks"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"

Connected through hyperedge HE-8 (induced by source paper "learning numeral embeddings"):
  with members:
    - "do nlp models know numbers probing numeracy in embeddings"
    - "numeracy for language models evaluating and improving their ability to predict numbers"
    - "ncrf an open source neural sequence labeling toolkit"
    - "numerically grounded language models for semantic error correction"
    - "solving general arithmetic word problems"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "efficient estimation of word representations in vector space"
    - "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)