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: "learning beyond datasets knowledge graph augmented neural networks for natural language processing"
  Abstract: Machine Learning has been the quintessential solution for many AI problems, but learning is still heavily dependent on the specific training data. Some learning models can be incorporated with a prior knowledge in the Bayesian set up, but these learning models do not have the ability to access any organised world knowledge on demand. In this work, we propose to enhance learning models with world knowledge in the form of Knowledge Graph (KG) fact triples for Natural Language Processing (NLP) tasks. Our aim is to develop a deep learning model that can extract relevant prior support facts from knowledge graphs depending on the task using attention mechanism. We introduce a convolution-based model for learning representations of knowledge graph entity and relation clusters in order to reduce the attention space. We show that the proposed method is highly scalable to the amount of prior information that has to be processed and can be applied to any generic NLP task. Using this method we show significant improvement in performance for text classification with News20, DBPedia datasets and natural language inference with Stanford Natural Language Inference (SNLI) dataset. We also demonstrate that a deep learning model can be trained well with substantially less amount of labeled training data, when it has access to organised world knowledge in the form of knowledge graph.

Connected through hyperedge HE-1 (induced by source paper "augmenting neural machine translation with knowledge graphs"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "machine translation using semantic web technologies a survey"
    - "an evaluation of neural machine translation models on historical spelling normalization"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "learning word vectors for 157 languages"
    - "the importance of being recurrent for modeling hierarchical structure"
    - "an analysis of attention mechanisms the case of word sense disambiguation in neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "kagnet knowledge aware graph networks for commonsense reasoning"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "does it make sense and why a pilot study for sense making and explanation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "improving question answering by commonsense based pre training"
    - "from recognition to cognition visual commonsense reasoning"
    - "graph convolution over pruned dependency trees improves relation extraction"

Connected through hyperedge HE-3 (induced by source paper "cooperative reasoning on knowledge graph and corpus a multi agentreinforcement learning approach"):
  with members:
    - "two body problem collaborative visual task completion"
    - "learning when not to answer a ternary reward structure for reinforcement learning based question answering"
    - "deep communicating agents for abstractive summarization"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "m walk learning to walk over graphs using monte carlo tree search"
    - "collaborative deep reinforcement learning for joint object search"
    - "question answering on knowledge bases and text using universal schema and memory networks"

Connected through hyperedge HE-4 (induced by source paper "improving question answering by commonsense based pre training"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "yuanfudao at semeval 2018 task 11 three way attention and relational knowledge for commonsense machine comprehension"
    - "a systematic classification of knowledge reasoning and context within the arc dataset"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "kg 2 learning to reason science exam questions with contextual knowledge graph embeddings"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"

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)