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 using explicit context conditioning"
  Abstract: Relation Extraction (RE) aims to label relations between groups of marked entities in raw text. Most current RE models learn context-aware representations of the target entities that are then used to establish relation between them. This works well for intra-sentence RE and we call them first-order relations. However, this methodology can sometimes fail to capture complex and long dependencies. To address this, we hypothesize that at times two target entities can be explicitly connected via a context token. We refer to such indirect relations as second-order relations and describe an efficient implementation for computing them. These second-order relation scores are then combined with first-order relation scores. Our empirical results show that the proposed method leads to state-of-the-art performance over two biomedical datasets.

Connected through hyperedge HE-1 (induced by source paper "assessment of amazon comprehend medical medication information extraction"):
  with members:
    - "dynamic transfer learning for named entity recognition"
    - "improving hospital mortality prediction with medical named entities and multimodal learning"

Connected through hyperedge HE-2 (induced by source paper "connecting the dots document level neural relation extraction with edge oriented graphs"):
  with members:
    - "document level n ary relation extraction with multiscale representation learning"
    - "a walk based model on entity graphs for relation extraction"
    - "a general framework for information extraction using dynamic span graphs"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "neural relation extraction within and across sentence boundaries"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "convolutional neural networks for chemical disease relation extraction are improved with character based word embeddings"

Connected through hyperedge HE-3 (induced by source paper "comprehend medical a named entity recognition and relationship extraction web service"):
  with members:
    - "a walk based model on entity graphs for relation extraction"
    - "joint entity extraction and assertion detection for clinical text"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "dynamic transfer learning for named entity recognition"
    - "a hybrid deep learning approach for medical relation extraction"
    - "improving hospital mortality prediction with medical named entities and multimodal learning"
    - "global normalization of convolutional neural networks for joint entity and relation classification"
    - "joint extraction of entities and relations based on a novel tagging scheme"

Connected through hyperedge HE-4 (induced by source paper "joint entity extraction and assertion detection for clinical text"):
  with members:
    - "comprehend medical a named entity recognition and relationship extraction web service"
    - "dynamic transfer learning for named entity recognition"
    - "improving hospital mortality prediction with medical named entities and multimodal learning"
    - "negbio a high performance tool for negation and uncertainty detection in radiology reports"
    - "morphological priors for probabilistic neural word embeddings"
    - "enriching word vectors with subword information"
    - "multi task cross lingual sequence tagging from scratch"
    - "multi task domain adaptation for sequence tagging"

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)