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: "joint embedding of hierarchical categories and entities for concept categorization and dataless classification"
  Abstract: Due to the lack of structured knowledge applied in learning distributed representation of cate- gories, existing work cannot incorporate category hierarchies into entity information. We propose a framework that embeds entities and categories into a semantic space by integrating structured knowledge and taxonomy hierarchy from large knowledge bases. The framework allows to com- pute meaningful semantic relatedness between entities and categories. Our framework can han- dle both single-word concepts and multiple-word concepts with superior performance on concept categorization and yield state of the art results on dataless hierarchical classification.

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"
    - "representation learning of entities and documents from knowledge base descriptions"
    - "learning distributed representations of texts and entities from knowledge base"
    - "named entity disambiguation for noisy text"

Connected through hyperedge HE-2 (induced by source paper "transition based dependency parsing using perceptron learner"):
  with members:
    - "a correspondence analysis framework for author conference recommendations"
    - "modeling product search relevance in e commerce"
    - "simultaneous identification of tweet purpose and position"
    - "an unsupervised domain independent framework for automated detection of persuasion tactics in text"
    - "a machine learning framework for authorship identification from texts"
    - "a heterogeneous graphical model to understand user level sentiments in social media"
    - "event outcome prediction using sentiment analysis and crowd wisdom in microblog feeds"
    - "object sensitive deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "modeling product search relevance in e commerce"):
  with members:
    - "a correspondence analysis framework for author conference recommendations"
    - "simultaneous identification of tweet purpose and position"
    - "an unsupervised domain independent framework for automated detection of persuasion tactics in text"
    - "a machine learning framework for authorship identification from texts"
    - "a heterogeneous graphical model to understand user level sentiments in social media"
    - "event outcome prediction using sentiment analysis and crowd wisdom in microblog feeds"
    - "object sensitive deep reinforcement learning"
    - "transparency and explanation in deep reinforcement learning neural networks"

Connected through hyperedge HE-4 (induced by source paper "a correspondence analysis framework for author conference recommendations"):
  with members:
    - "simultaneous identification of tweet purpose and position"
    - "an unsupervised domain independent framework for automated detection of persuasion tactics in text"
    - "a machine learning framework for authorship identification from texts"
    - "a heterogeneous graphical model to understand user level sentiments in social media"
    - "event outcome prediction using sentiment analysis and crowd wisdom in microblog feeds"
    - "object sensitive deep reinforcement learning"
    - "transparency and explanation in deep reinforcement learning neural networks"
    - "photorealistic style transfer for videos"

Connected through hyperedge HE-5 (induced by source paper "simultaneous identification of tweet purpose and position"):
  with members:
    - "object sensitive deep reinforcement learning"
    - "transparency and explanation in deep reinforcement learning neural networks"

Connected through hyperedge HE-6 (induced by source paper "learning hierarchy aware knowledge graph embeddings for link prediction"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "ernie enhanced language representation with informative entities"
    - "learning to exploit long term relational dependencies in knowledge graphs"
    - "a capsule network based embedding model for knowledge graph completion and search personalization"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "analogical inference for multi relational embeddings"
    - "convolutional 2d knowledge graph embeddings"

Connected through hyperedge HE-7 (induced by source paper "benchmarking zero shot text classification datasets evaluation and entailment approach"):
  with members:
    - "integrating semantic knowledge to tackle zero shot text classification"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "fever a large scale dataset for fact extraction and verification"
    - "zero shot sequence labeling transferring knowledge from sentences to tokens"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "zero shot user intent detection via capsule neural networks"
    - "train once test anywhere zero shot learning for text classification"
    - "zero shot relation extraction via reading comprehension"

Connected through hyperedge HE-8 (induced by source paper "an unsupervised domain independent framework for automated detection of persuasion tactics in text"):
  with members:
    - "event outcome prediction using sentiment analysis and crowd wisdom in microblog feeds"
    - "object sensitive deep reinforcement learning"
    - "transparency and explanation in deep reinforcement learning neural networks"
    - "photorealistic style transfer for videos"
    - "argument strength is in the eye of the beholder audience effects in persuasion"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "content based video indexing and retrieval using corr lda"
    - "distributed representations of sentences and documents"

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)