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: "representation learning of entities and documents from knowledge base descriptions"
  Abstract: In this paper, we describe TextEnt, a neural network model that learns distributed representations of entities and documents directly from a knowledge base (KB). Given a document in a KB consisting of words and entity annotations, we train our model to predict the entity that the document describes and map the document and its target entity close to each other in a continuous vector space. Our model is trained using a large number of documents extracted from Wikipedia. The performance of the proposed model is evaluated using two tasks, namely fine-grained entity typing and multiclass text classification. The results demonstrate that our model achieves state-of-the-art performance on both tasks. The code and the trained representations are made available online for further academic research.

Connected through hyperedge HE-1 (induced by source paper "wikipedia2vec an efficient toolkit for learning and visualizing the embeddings of words and entities from wikipedia"):
  with members:
    - "ernie enhanced language representation with informative entities"
    - "yelm end to end contextualized entity linking"
    - "neural attentive bag of entities model for text classification"
    - "studio ousia s quiz bowl question answering system"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning distributed representations of texts and entities from knowledge base"
    - "named entity disambiguation for noisy text"
    - "deep joint entity disambiguation with local neural attention"

Connected through hyperedge HE-2 (induced by source paper "representation learning for words and entities"):
  with members:
    - "deep multi view learning to rank"
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "deep contextualized word representations"
    - "deep sets"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "new embedded representations and evaluation protocols for inferring transitive relations"
    - "neural collaborative filtering"
    - "stochastic canonical correlation analysis"

Connected through hyperedge HE-3 (induced by source paper "neural attentive bag of entities model for text classification"):
  with members:
    - "neural collective entity linking"
    - "end to end neural entity linking"
    - "studio ousia s quiz bowl question answering system"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "wikipedia2vec an optimized tool for learning embeddings of words and entities from wikipedia"
    - "learning distributed representations of texts and entities from knowledge base"
    - "named entity disambiguation for noisy text"
    - "deep joint entity disambiguation with local neural attention"

Connected through hyperedge HE-4 (induced by source paper "wikipedia2vec an optimized tool for learning embeddings of words and entities from wikipedia"):
  with members:
    - "an open world extension to knowledge graph completion models"
    - "bert is not a knowledge base yet factual knowledge vs name based reasoning in unsupervised qa"
    - "ernie enhanced language representation with informative entities"
    - "knowledge enhanced contextual word representations"
    - "yelm end to end contextualized entity linking"
    - "on the coherence of fake news articles"
    - "learning word vectors for 157 languages"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)