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: "intrinsic subspace evaluation of word embedding representations"
  Abstract: We introduce a new methodology for intrinsic evaluation of word representations. Specifically, we identify four fundamental criteria based on the characteristics of natural language that pose difficulties to NLP systems; and develop tests that directly show whether or not representations contain the subspaces necessary to satisfy these criteria. Current intrinsic evaluations are mostly based on the overall similarity or full-space similarity of words and thus view vector representations as points. We show the limits of these point-based intrinsic evaluations. We apply our evaluation methodology to the comparison of a count vector model and several neural network models and demonstrate important properties of these models.

Connected through hyperedge HE-1 (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-2 (induced by source paper "evaluating word embedding models methods and experimental results"):
  with members:
    - "cross lingual language model pretraining"
    - "unsupervised statistical machine translation"
    - "semantic structure and interpretability of word embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a survey of word embeddings evaluation methods"
    - "post processing of word representations via variance normalization and dynamic embedding"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "analyzing structures in the semantic vector space a framework for decomposing word embeddings"):
  with members:
    - "imparting interpretability to word embeddings while preserving semantic structure"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "concatenated p mean word embeddings as universal cross lingual sentence representations"
    - "rotations and interpretability of word embeddings the case of the russian language"
    - "recent trends in deep learning based natural language processing"
    - "linear algebraic structure of word senses with applications to polysemy"
    - "ultradense word embeddings by orthogonal transformation"

Connected through hyperedge HE-4 (induced by source paper "from word to sense embeddings a survey on vector representations of meaning"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "seven augmenting word embeddings with unsupervised relation vectors"
    - "incorporating glosses into neural word sense disambiguation"
    - "deep contextualized word representations"
    - "wic 10 000 example pairs for evaluating context sensitive representations"
    - "knowledge based word sense disambiguation using topic models"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

Connected through hyperedge HE-5 (induced by source paper "evaluating word embeddings in multi label classification using fine grained name typing"):
  with members:
    - "do multi sense embeddings improve natural language understanding"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "firearms and tigers are dangerous kitchen knives and zebras are not testing whether word embeddings can tell"):
  with members:
    - "correlation based intrinsic evaluation of word vector representations"
    - "issues in evaluating semantic spaces using word analogies"

Connected through hyperedge HE-7 (induced by source paper "hyperlex a large scale evaluation of graded lexical entailment"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "morph fitting fine tuning word vector spaces with simple language specific rules"
    - "baselines and test data for cross lingual inference"
    - "bilingual learning of multi sense embeddings with discrete autoencoders"
    - "relations such as hypernymy identifying and exploiting hearst patterns in distributional vectors for lexical entailment"
    - "a decomposable attention model for natural language inference"
    - "counter fitting word vectors to linguistic constraints"
    - "problems with evaluation of word embeddings using word similarity tasks"

Connected through hyperedge HE-8 (induced by source paper "noise mitigation for neural entity typing and relation extraction"):
  with members:
    - "label noise reduction in entity typing by heterogeneous partial label embedding"
    - "inferring missing entity type instances for knowledge base completion new dataset and methods"
    - "a convolutional neural network for modelling sentences"
    - "connecting language and knowledge bases with embedding models for relation extraction"
    - "efficient estimation of word representations in vector space"
    - "natural language processing almost from scratch"

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)