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 representation learning of text and knowledge for knowledge graph completion"
  Abstract: Joint representation learning of text and knowledge within a unified semantic space enables us to perform knowledge graph completion more accurately. In this work, we propose a novel framework to embed words, entities and relations into the same continuous vector space. In this model, both entity and relation embeddings are learned by taking knowledge graph and plain text into consideration. In experiments, we evaluate the joint learning model on three tasks including entity prediction, relation prediction and relation classification from text. The experiment results show that our model can significantly and consistently improve the performance on the three tasks as compared with other baselines.

Connected through hyperedge HE-1 (induced by source paper "is aligning embedding spaces a challenging task an analysis of the existing methods"):
  with members:
    - "joint representation learning of cross lingual words and entities via attentive distant supervision"
    - "word translation without parallel data"
    - "multilingual knowledge graph embeddings for cross lingual knowledge alignment"
    - "joint learning of the embedding of words and entities for named entity disambiguation"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "efficient estimation of word representations in vector space"
    - "exploiting similarities among languages for machine translation"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-2 (induced by source paper "kepler a unified model for knowledge embedding and pre trained language representation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "graphvite a high performance cpu gpu hybrid system for node embedding"
    - "language models as knowledge bases"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "multi task deep neural networks for natural language understanding"
    - "matching the blanks distributional similarity for relation learning"

Connected through hyperedge HE-3 (induced by source paper "ernie enhanced language representation with informative entities"):
  with members:
    - "ernie enhanced representation through knowledge integration"
    - "improving question answering by commonsense based pre training"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "joint representation learning of cross lingual words and entities via attentive distant supervision"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "ultra fine entity typing"

Connected through hyperedge HE-4 (induced by source paper "k bert enabling language representation with knowledge graph"):
  with members:
    - "ernie enhanced representation through knowledge integration"
    - "spanbert improving pre training by representing and predicting spans"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "transformer xl attentive language models beyond a fixed length context"
    - "ernie enhanced language representation with informative entities"
    - "pre training with whole word masking for chinese bert"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-5 (induced by source paper "collaborative policy learning for open knowledge graph reasoning"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "on constrained open world probabilistic databases"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "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"
    - "open world knowledge graph completion"
    - "convolutional 2d knowledge graph embeddings"

Connected through hyperedge HE-6 (induced by source paper "knowledge representation learning a quantitative review"):
  with members:
    - "improving visual relationship detection using semantic modeling of scene descriptions"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "flexible end to end dialogue system for knowledge grounded conversation"
    - "a neural knowledge language model"
    - "analogical inference for multi relational embeddings"
    - "visual translation embedding network for visual relation detection"
    - "probabilistic reasoning via deep learning neural association models"

Connected through hyperedge HE-7 (induced by source paper "open domain question answering using early fusion of knowledge bases and text"):
  with members:
    - "weaver deep co encoding of questions and documents for machine reading"
    - "the web as a knowledge base for answering complex questions"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "walk steered convolution for graph classification"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "reinforced mnemonic reader for machine comprehension"
    - "object oriented neural programming oonp for document understanding"
    - "improved neural relation detection for knowledge base question answering"

Connected through hyperedge HE-8 (induced by source paper "semantically enhanced models for commonsense knowledge acquisition"):
  with members:
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "node2vec scalable feature learning for networks"
    - "modeling relation paths for representation learning of knowledge bases"
    - "line large scale information network embedding"
    - "traversing knowledge graphs in vector space"
    - "deepwalk online learning of social representations"
    - "retrofitting word vectors to semantic lexicons"
    - "efficient estimation of word representations 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)