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: "attending to all mention pairs for full abstract biological relation extraction"
  Abstract: Most work in relation extraction forms a prediction by looking at a short span of text within a single sentence containing a single entity pair mention. However, many relation types, particularly in biomedical text, are expressed across sentences or require a large context to disambiguate. We propose a model to consider all mention and entity pairs simultaneously in order to make a prediction. We encode full paper abstracts using an efficient self-attention encoder and form pairwise predictions between all mentions with a bi-affine operation. An entity-pair wise pooling aggregates mention pair scores to make a final prediction while alleviating training noise by performing within document multi-instance learning. We improve our model's performance by jointly training the model to predict named entities and adding an additional corpus of weakly labeled data. We demonstrate our model's effectiveness by achieving the state of the art on the Biocreative V Chemical Disease Relation dataset for models without KB resources, outperforming ensembles of models which use hand-crafted features and additional linguistic resources.

Connected through hyperedge HE-1 (induced by source paper "knowledge guided convolutional networks for chemical disease relation extraction"):
  with members:
    - "chemical induced disease relation extraction with dependency information and prior knowledge"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "convolutional neural networks for chemical disease relation extraction are improved with character based word embeddings"
    - "aspect level sentiment classification with deep memory network"
    - "conditional image generation with pixelcnn decoders"
    - "language modeling with gated convolutional networks"
    - "adam a method for stochastic optimization"
    - "a convolutional neural network for modelling sentences"

Connected through hyperedge HE-2 (induced by source paper "glomo unsupervisedly learned relational graphs as transferable representations"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "neural models for reasoning over multiple mentions using coreference"
    - "deep contextualized word representations"
    - "relational inductive biases deep learning and graph networks"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "stochastic answer networks for natural language inference"
    - "linguistically informed self attention for semantic role labeling"
    - "non local neural networks"

Connected through hyperedge HE-3 (induced by source paper "convolutional neural networks for chemical disease relation extraction are improved with character based word embeddings"):
  with members:
    - "relation extraction a survey"
    - "deep residual learning for weakly supervised relation extraction"
    - "attention is all you need"
    - "a novel neural network model for joint pos tagging and graph based dependency parsing"
    - "incorporating relation paths in neural relation extraction"
    - "distant supervision for relation extraction beyond the sentence boundary"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "quasi recurrent 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)