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 general framework for information extraction using dynamic span graphs"
  Abstract: We introduce a general framework for several information extraction tasks that share span representations using dynamically constructed span graphs. The graphs are constructed by selecting the most confident entity spans and linking these nodes with confidence-weighted relation types and coreferences. The dynamic span graph allows coreference and relation type confidences to propagate through the graph to iteratively refine the span representations. This is unlike previous multi-task frameworks for information extraction in which the only interaction between tasks is in the shared first-layer LSTM. Our framework significantly outperforms the state-of-the-art on multiple information extraction tasks across multiple datasets reflecting different domains. We further observe that the span enumeration approach is good at detecting nested span entities, with significant F1 score improvement on the ACE dataset.

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"
    - "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"
    - "joint entity recognition and relation extraction as a multi head selection problem"

Connected through hyperedge HE-2 (induced by source paper "generalizing natural language analysis through span relation representations"):
  with members:
    - "enriching pre trained language model with entity information for relation classification"
    - "bert rediscovers the classical nlp pipeline"
    - "spanbert improving pre training by representing and predicting spans"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "multi task deep neural networks for natural language understanding"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "simple bert models for relation extraction and semantic role labeling"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"

Connected through hyperedge HE-3 (induced by source paper "a unified mrc framework for named entity recognition"):
  with members:
    - "entity relation extraction as multi turn question answering"
    - "glyce glyph vectors for chinese character representations"
    - "nested named entity recognition via second best sequence learning and decoding"
    - "sequence to nuggets nested entity mention detection via anchor region networks"
    - "merge and label a novel neural network architecture for nested ner"
    - "neural architectures for nested ner through linearization"
    - "chinese ner using lattice lstm"
    - "the natural language decathlon multitask learning as question answering"

Connected through hyperedge HE-4 (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-5 (induced by source paper "entity relation and event extraction with contextualized span representations"):
  with members:
    - "a walk based model on entity graphs for relation extraction"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "one for all neural joint modeling of entities and events"
    - "allennlp a deep semantic natural language processing platform"
    - "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 "connecting the dots document level neural relation extraction with edge oriented graphs"):
  with members:
    - "relation extraction using explicit context conditioning"
    - "document level n ary relation extraction with multiscale representation learning"
    - "a walk based model on entity graphs for relation extraction"
    - "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-7 (induced by source paper "merge and label a novel neural network architecture for nested ner"):
  with members:
    - "neural segmental hypergraphs for overlapping mention recognition"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "self normalizing neural networks"
    - "attention is all you need"
    - "labeling gaps between words recognizing overlapping mentions with mention separators"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-8 (induced by source paper "sept improving scientific named entity recognition with span representation"):
  with members:
    - "a survey on recent advances in named entity recognition from deep learning models"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end neural coreference resolution"
    - "end to end sequence labeling via bi directional lstm cnns crf"

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)