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: "a survey of deep learning methods for relation extraction"
  Abstract: Relation Extraction is an important sub-task of Information Extraction which has the potential of employing deep learning (DL) models with the creation of large datasets using distant supervision. In this review, we compare the contributions and pitfalls of the various DL models that have been used for the task, to help guide the path ahead.

Connected through hyperedge HE-1 (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"
    - "relation extraction from clinical texts using domain invariant convolutional neural network"
    - "end to end relation extraction using lstms on sequences and tree structures"

Connected through hyperedge HE-2 (induced by source paper "relation extraction using explicit context conditioning"):
  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"
    - "self attention with relative position representations"
    - "a hybrid deep learning approach for medical relation extraction"
    - "improving hospital mortality prediction with medical named entities and multimodal learning"
    - "attention is all you need"
    - "joint extraction of entities and relations based on a novel tagging scheme"

Connected through hyperedge HE-3 (induced by source paper "a study of recent contributions on information extraction"):
  with members:
    - "deep learning applied to nlp"
    - "a brief survey of text mining classification clustering and extraction techniques"
    - "relation extraction from clinical texts using domain invariant convolutional neural network"
    - "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)