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: "simultaneously self attending to all mentions 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. This approach often does not consider interactions across mentions, requires redundant computation for each mention pair, and ignores relationships expressed across sentence boundaries. These problems are exacerbated by the document- (rather than sentence-) level annotation common in biological text. In response, we propose a model which simultaneously predicts relationships between all mention pairs in a document. We form pairwise predictions over entire paper abstracts using an efficient self-attention encoder. All-pairs mention scores allow us to perform multi-instance learning by aggregating over mentions to form entity pair representations. We further adapt to settings without mention-level annotation by jointly training to predict named entities and adding a corpus of weakly labeled data. In experiments on two Biocreative benchmark datasets, we achieve state of the art performance on the Biocreative V Chemical Disease Relation dataset for models without external KB resources. We also introduce a new dataset an order of magnitude larger than existing human-annotated biological information extraction datasets and more accurate than distantly supervised alternatives.

Connected through hyperedge HE-1 (induced by source paper "leveraging dependency forest for neural medical relation extraction"):
  with members:
    - "graph based neural sentence ordering"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "a silver standard corpus of human phenotype gene relations"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "semantic neural machine translation using amr"
    - "attention guided graph convolutional networks for relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "exploiting rich syntactic information for semantic parsing with graph to sequence model"

Connected through hyperedge HE-2 (induced by source paper "attention guided graph convolutional networks for relation extraction"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "densely connected graph convolutional networks for graph to sequence learning"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "representation learning on graphs with jumping knowledge networks"
    - "n ary relation extraction using graph state lstm"
    - "cross sentence n ary relation extraction with graph lstms"
    - "end to end neural coreference resolution"
    - "improved neural relation detection for knowledge base question answering"

Connected through hyperedge HE-3 (induced by source paper "span based joint entity and relation extraction with transformer pre training"):
  with members:
    - "a survey on recent advances in named entity recognition from deep learning models"
    - "spanbert improving pre training by representing and predicting spans"
    - "scibert a pretrained language model for scientific text"
    - "neural metric learning for fast end to end relation extraction"
    - "entity relation extraction as multi turn question answering"
    - "mass masked sequence to sequence pre training for language generation"
    - "transformer xl attentive language models beyond a fixed length context"
    - "end to end neural relation extraction using deep biaffine attention"

Connected through hyperedge HE-4 (induced by source paper "learning to infer entities properties and their relations from clinical conversations"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "extracting symptoms and their status from clinical conversations"
    - "neural relation extraction within and across sentence boundaries"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "multi labeled relation extraction with attentive capsule network"
    - "multi task learning for sequence tagging an empirical study"
    - "a hierarchical framework for relation extraction with reinforcement learning"
    - "the best of both worlds combining recent advances in neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "fine tuning pre trained transformer language models to distantly supervised relation extraction"):
  with members:
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "generating wikipedia by summarizing long sequences"
    - "deep contextualized word representations"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "see syntax aware entity embedding for neural relation extraction"
    - "universal language model fine tuning for text classification"
    - "neural relation extraction via inner sentence noise reduction and transfer learning"

Connected through hyperedge HE-6 (induced by source paper "improving relation extraction with knowledge attention"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "position aware self attention with relative positional encodings for slot filling"
    - "on2vec embedding based relation prediction for ontology population"
    - "multi level structured self attentions for distantly supervised relation extraction"
    - "improved neural relation detection for knowledge base question answering"
    - "neural natural language inference models enhanced with external knowledge"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "construct dynamic graphs for hand gesture recognition via spatial temporal attention"):
  with members:
    - "semantic graph convolutional networks for 3d human pose regression"
    - "quantized densely connected u nets for efficient landmark localization"
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "learning to forecast and refine residual motion for image to video generation"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "cr gan learning complete representations for multi view generation"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"

Connected through hyperedge HE-8 (induced by source paper "water supply prediction based on initialized attention residual network"):
  with members:
    - "link prediction with mutual attention for text attributed networks"
    - "learning data augmentation strategies for object detection"
    - "random erasing data augmentation"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "language modeling with gated convolutional networks"
    - "neural machine translation in linear time"

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)