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: "contextualized non local neural networks for sequence learning"
  Abstract: Recently, a large number of neural mechanisms and models have been proposed for sequence learning, of which self-attention, as exemplified by the Transformer model, and graph neural networks (GNNs) have attracted much attention. In this paper, we propose an approach that combines and draws on the complementary strengths of these two methods. Specifically, we propose contextualized non-local neural networks (CN$^{\textbf{3}}$), which can both dynamically construct a task-specific structure of a sentence and leverage rich local dependencies within a particular neighborhood. #R##N#Experimental results on ten NLP tasks in text classification, semantic matching, and sequence labeling show that our proposed model outperforms competitive baselines and discovers task-specific dependency structures, thus providing better interpretability to users.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning based graph to sequence model for natural question generation"):
  with members:
    - "bidirectional attentive memory networks for question answering over knowledge bases"
    - "learning to generate questions by learningwhat not to generate"
    - "graphflow exploiting conversation flow with graph neural networks for conversational machine comprehension"
    - "reinforcement learning based text style transfer without parallel training corpus"
    - "deep iterative and adaptive learning for graph neural networks"
    - "unified language model pre training for natural language understanding and generation"
    - "sql to text generation with graph to sequence model"
    - "towards a better metric for evaluating question generation systems"

Connected through hyperedge HE-2 (induced by source paper "star transformer"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "sequence labeling a practical approach"
    - "multi task learning over graph structures"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dynamic self attention computing attention over words dynamically for sentence embedding"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-3 (induced by source paper "hierarchical contextualized representation for named entity recognition"):
  with members:
    - "semantics aware bert for language understanding"
    - "grn gated relation network to enhance convolutional neural network for named entity recognition"
    - "syntax aware multilingual semantic role labeling"
    - "lattice based transformer encoder for neural machine translation"
    - "gcdt a global context enhanced deep transition architecture for sequence labeling"
    - "dual co matching network for multi choice reading comprehension"
    - "robust lexical features for improved neural network named entity recognition"
    - "deep contextualized word representations"

Connected through hyperedge HE-4 (induced by source paper "multi hop reading comprehension across multiple documents by reasoning over heterogeneous graphs"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "global to local memory pointer networks for task oriented dialogue"
    - "densely connected attention propagation for reading comprehension"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "neural models for reasoning over multiple mentions using coreference"

Connected through hyperedge HE-5 (induced by source paper "graphflow exploiting conversation flow with graph neural networks for conversational machine comprehension"):
  with members:
    - "sdnet contextualized attention based deep network for conversational question answering"
    - "sql to text generation with graph to sequence model"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "a qualitative comparison of coqa squad 2 0 and quac"
    - "exploiting rich syntactic information for semantic parsing with graph to sequence model"
    - "coqa a conversational question answering challenge"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "open domain question answering using early fusion of knowledge bases and text"

Connected through hyperedge HE-6 (induced by source paper "tener adapting transformer encoder for named entity recognition"):
  with members:
    - "can ner convolutional attention network for chinese named entity recognition"
    - "grn gated relation network to enhance convolutional neural network for named entity recognition"
    - "transformer xl attentive language models beyond a fixed length context"
    - "sequence labeling a practical approach"
    - "chinese ner using lattice lstm"
    - "self attention with relative position representations"
    - "robust lexical features for improved neural network named entity recognition"
    - "hybrid semi markov crf for neural sequence labeling"

Connected through hyperedge HE-7 (induced by source paper "deep iterative and adaptive learning for graph neural networks"):
  with members:
    - "graphflow exploiting conversation flow with graph neural networks for conversational machine comprehension"
    - "graph convolutional transformer learning the graphical structure of electronic health records"
    - "learning discrete structures for graph neural networks"
    - "reinforcement learning based graph to sequence model for natural question generation"
    - "dyngraph2seq dynamic graph to sequence interpretable learning for health stage prediction in online health forums"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sql to text generation with graph to sequence model"
    - "adaptive graph convolutional neural networks"

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)