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: "relation extraction from clinical texts using domain invariant convolutional neural network"
  Abstract: In recent years extracting relevant information from biomedical and clinical texts such as research articles, discharge summaries, or electronic health records have been a subject of many research efforts and shared challenges. Relation extraction is the process of detecting and classifying the semantic relation among entities in a given piece of texts. Existing models for this task in biomedical domain use either manually engineered features or kernel methods to create feature vector. These features are then fed to classifier for the prediction of the correct class. It turns out that the results of these methods are highly dependent on quality of user designed features and also suffer from curse of dimensionality. In this work we focus on extracting relations from clinical discharge summaries. Our main objective is to exploit the power of convolution neural network (CNN) to learn features automatically and thus reduce the dependency on manual feature engineering. We evaluate performance of the proposed model on i2b2-2010 clinical relation extraction challenge dataset. Our results indicate that convolution neural network can be a good model for relation exaction in clinical text without being dependent on expert's knowledge on defining quality features.

Connected through hyperedge HE-1 (induced by source paper "reflex flexible framework for relation extraction in multiple domains"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "publicly available clinical bert embeddings"
    - "scispacy fast and robust models for biomedical natural language processing"
    - "classifying medical relations in clinical text via convolutional neural networks"
    - "eth ds3lab at semeval 2018 task 7 effectively combining recurrent and convolutional neural networks for relation classification and extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "fine tuning bert for joint entity and relation extraction in chinese medical text"):
  with members:
    - "fine grained named entity recognition using elmo and wikidata"
    - "improving relation extraction by pre trained language representations"
    - "deep contextualized word representations"
    - "automatic severity classification of coronary artery disease via recurrent capsule network"
    - "attention is all you need"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "a survey of deep learning methods for relation extraction"
    - "end to end relation extraction using lstms on sequences and tree structures"

Connected through hyperedge HE-3 (induced by source paper "classifying medical relations in clinical text via convolutional neural networks"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "drug drug interaction extraction from biomedical text using long short term memory network"
    - "improved relation classification by deep recurrent neural networks with data augmentation"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "classifying relations by ranking with convolutional neural networks"
    - "local translation prediction with global sentence representation"
    - "classifying relations via long short term memory networks along shortest dependency path"
    - "task oriented learning of word embeddings for semantic relation classification"

Connected through hyperedge HE-4 (induced by source paper "a self attention based deep learning method for lesion attribute detection from ct reports"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "attention guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs"
    - "biosentvec creating sentence embeddings for biomedical texts"
    - "attention is all you need"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "deep learning for extracting protein protein interactions from biomedical literature"
    - "show attend and tell neural image caption generation with visual attention"
    - "interleaved text image deep mining on a large scale radiology database for automated image interpretation"

Connected through hyperedge HE-5 (induced by source paper "gnteam at 2018 n2c2 feature augmented bilstm crf for drug related entity recognition in hospital discharge summaries"):
  with members:
    - "ernie enhanced language representation with informative entities"
    - "extracting adverse drug reactions and their context using sequence labelling ensembles in tac2017"
    - "deep contextualized word representations"
    - "feature augmented neural networks for patient note de identification"
    - "structured prediction models for rnn based sequence labeling in clinical text"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "relation extraction between the clinical entities based on the shortest dependency path based lstm"):
  with members:
    - "feature assisted bi directional lstm model for protein protein interaction identification from biomedical texts"
    - "semantic relation classification via convolutional neural networks with simple negative sampling"
    - "distributed representations of words and phrases and their compositionality"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "convolutional gated recurrent units for medical relation classification"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "building a comprehensive syntactic and semantic corpus of chinese clinical texts"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "combining recurrent and convolutional neural networks for relation classification"
    - "combining neural networks and log linear models to improve relation extraction"
    - "classifying relations by ranking with convolutional neural networks"
    - "classifying relations via long short term memory networks along shortest dependency path"
    - "a c lstm neural network for text classification"

Connected through hyperedge HE-8 (induced by source paper "a study of recent contributions on information extraction"):
  with members:
    - "deep learning applied to nlp"
    - "a survey of deep learning methods for relation extraction"
    - "a brief survey of text mining classification clustering and extraction techniques"
    - "combining neural networks and log linear models to improve relation extraction"

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)