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: "adversarial training for multi context joint entity and relation extraction"
  Abstract: Adversarial training (AT) is a regularization method that can be used to improve the robustness of neural network methods by adding small perturbations in the training data. We show how to use AT for the tasks of entity recognition and relation extraction. In particular, we demonstrate that applying AT to a general purpose baseline model for jointly extracting entities and relations, allows improving the state-of-the-art effectiveness on several datasets in different contexts (i.e., news, biomedical, and real estate data) and for different languages (English and Dutch).

Connected through hyperedge HE-1 (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-2 (induced by source paper "entity relation extraction as multi turn question answering"):
  with members:
    - "the natural language decathlon multitask learning as question answering"
    - "deep contextualized word representations"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a hierarchical framework for relation extraction with reinforcement learning"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "improving machine reading comprehension via adversarial training"):
  with members:
    - "option comparison network for multiple choice reading comprehension"
    - "spanbert improving pre training by representing and predicting spans"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "robust machine comprehension models via adversarial training"
    - "exploring machine reading comprehension with explicit knowledge"
    - "know what you don t know unanswerable questions for squad"
    - "coqa a conversational question answering challenge"

Connected through hyperedge HE-4 (induced by source paper "a review of keyphrase extraction"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "keyphrase generation with correlation constraints"
    - "learning feature representations for keyphrase extraction"
    - "semi supervised learning for neural keyphrase generation"
    - "simple unsupervised keyphrase extraction using sentence embeddings"
    - "joint entity recognition and relation extraction as a multi head selection problem"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"

Connected through hyperedge HE-5 (induced by source paper "a general framework for information extraction using dynamic span graphs"):
  with members:
    - "a walk based model on entity graphs for relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "neural segmental hypergraphs for overlapping mention recognition"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "an annotated corpus for machine reading of instructions in wet lab protocols"

Connected through hyperedge HE-6 (induced by source paper "integrating deep learning with logic fusion for information extraction"):
  with members:
    - "neural logic machines"
    - "deepproblog neural probabilistic logic programming"
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "deep probabilistic logic a unifying framework for indirect supervision"
    - "joint entity recognition and relation extraction as a multi head selection problem"
    - "adversarially regularising neural nli models to integrate logical background knowledge"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"

Connected through hyperedge HE-7 (induced by source paper "jointly learning entity and relation representations for entity alignment"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "relation aware entity alignment for heterogeneous knowledge graphs"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "semi supervised user geolocation via graph convolutional networks"
    - "co training embeddings of knowledge graphs and entity descriptions for cross lingual entity alignment"
    - "variational reasoning for question answering with knowledge graph"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "multilingual knowledge graph embeddings for cross lingual knowledge alignment"

Connected through hyperedge HE-8 (induced by source paper "end to end named entity recognition and relation extraction using pre trained language models"):
  with members:
    - "transfer learning in biomedical natural language processing an evaluation of bert and elmo on ten benchmarking datasets"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "entity relation extraction as multi turn question answering"
    - "enhancing clinical concept extraction with contextual embeddings"
    - "end to end neural relation extraction using deep biaffine attention"
    - "a multiscale visualization of attention in the transformer model"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "joint entity recognition and relation extraction as a multi head selection problem"

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)