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: "dolores deep contextualized knowledge graph embeddings"
  Abstract: We introduce a new method DOLORES for learning knowledge graph embeddings that effectively captures contextual cues and dependencies among entities and relations. First, we note that short paths on knowledge graphs comprising of chains of entities and relations can encode valuable information regarding their contextual usage. We operationalize this notion by representing knowledge graphs not as a collection of triples but as a collection of entity-relation chains, and learn embeddings for entities and relations using deep neural models that capture such contextual usage. In particular, our model is based on Bi-Directional LSTMs and learn deep representations of entities and relations from constructed entity-relation chains. We show that these representations can very easily be incorporated into existing models to significantly advance the state of the art on several knowledge graph prediction tasks like link prediction, triple classification, and missing relation type prediction (in some cases by at least 9.5%).

Connected through hyperedge HE-1 (induced by source paper "kg bert bert for knowledge graph completion"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "ernie enhanced language representation with informative entities"
    - "a multiscale visualization of attention in the transformer model"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"

Connected through hyperedge HE-2 (induced by source paper "a re evaluation of knowledge graph completion methods"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "learning attention based embeddings for relation prediction in knowledge graphs"
    - "tucker tensor factorization for knowledge graph completion"
    - "on the knowledge graph completion using translation based embedding the loss is as important as the score"
    - "toward understanding the effect of loss function on then performance of knowledge graph embedding"
    - "a capsule network based embedding model for knowledge graph completion and search personalization"
    - "end to end structure aware convolutional networks for knowledge base completion"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"

Connected through hyperedge HE-3 (induced by source paper "reasoning over paths via knowledge base completion"):
  with members:
    - "explainable knowledge graph based recommendation via deep reinforcement learning"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "chains of reasoning over entities relations and text using recurrent neural networks"
    - "complex embeddings for simple link prediction"
    - "traversing knowledge graphs in vector space"

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)