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: "joint entity recognition and relation extraction as a multi head selection problem"
  Abstract: Abstract   State-of-the-art models for joint entity recognition and relation extraction strongly rely on external natural language processing (NLP) tools such as POS (part-of-speech) taggers and dependency parsers. Thus, the performance of such joint models depends on the quality of the features obtained from these NLP tools. However, these features are not always accurate for various languages and contexts. In this paper, we propose a joint neural model which performs entity recognition and relation extraction simultaneously, without the need of any manually extracted features or the use of any external tool. Specifically, we model the entity recognition task using a CRF (Conditional Random Fields) layer and the relation extraction task as a multi-head selection problem (i.e., potentially identify multiple relations for each entity). We present an extensive experimental setup, to demonstrate the effectiveness of our method using datasets from various contexts (i.e., news, biomedical, real estate) and languages (i.e., English, Dutch). Our model outperforms the previous neural models that use automatically extracted features, while it performs within a reasonable margin of feature-based neural models, or even beats them.

Connected through hyperedge HE-1 (induced by source paper "deeper task specificity improves joint entity and relation extraction"):
  with members:
    - "span based joint entity and relation extraction with transformer pre training"
    - "an empirical evaluation of multi task learning in deep neural networks for natural language processing"
    - "branched multi task networks deciding what layers to share"
    - "entity relation extraction as multi turn question answering"
    - "a general framework for information extraction using dynamic span graphs"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (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-3 (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"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "adversarial training for multi context joint entity and relation extraction"

Connected through hyperedge HE-4 (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"
    - "adversarially regularising neural nli models to integrate logical background knowledge"
    - "adversarial training for multi context joint entity and relation extraction"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"

Connected through hyperedge HE-5 (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"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "end to end neural relation extraction using deep biaffine attention"):
  with members:
    - "adversarial training for multi context joint entity and relation extraction"
    - "global normalization of convolutional neural networks for joint entity and relation classification"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "deep biaffine attention for neural dependency parsing"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "neural architectures for named entity recognition"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "combining neural networks and log linear models to improve relation extraction"

Connected through hyperedge HE-7 (induced by source paper "neural metric learning for fast end to end relation extraction"):
  with members:
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "adversarial training for multi context joint entity and relation extraction"
    - "global normalization of convolutional neural networks for joint entity and relation classification"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "effective approaches to attention based neural machine translation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "named entity recognition with bidirectional lstm cnns"

Connected through hyperedge HE-8 (induced by source paper "bert based multi head selection for joint entity relation extraction"):
  with members:
    - "entity relation extraction as multi turn question answering"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "xgboost a scalable tree boosting system"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "semantic relation classification via convolutional neural networks with simple negative sampling"

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)