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 extraction of events and entities within a document context"
  Abstract: Events and entities are closely related; entities are often actors or participants in events and events without entities are uncommon. The interpretation of events and entities is highly contextually dependent. Existing work in information extraction typically models events separately from entities, and performs inference at the sentence level, ignoring the rest of the document. In this paper, we propose a novel approach that models the dependencies among variables of events, entities, and their relations, and performs joint inference of these variables across a document. The goal is to enable access to document-level contextual information and facilitate context-aware predictions. We demonstrate that our approach substantially outperforms the state-of-the-art methods for event extraction as well as a strong baseline for entity extraction.

Connected through hyperedge HE-1 (induced by source paper "joint event extraction along shortest dependency paths using graph convolutional networks"):
  with members:
    - "jointly multiple events extraction via attention based graph information aggregation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "rapid customization for event extraction"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "semi supervised classification with graph convolutional networks"
    - "a compare aggregate model for matching text sequences"
    - "extracting temporal and causal relations between events"

Connected through hyperedge HE-2 (induced by source paper "leveraging knowledge bases in lstms for improving machine reading"):
  with members:
    - "a neural knowledge language model"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "key value memory networks for directly reading documents"
    - "neural architectures for named entity recognition"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-3 (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-4 (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"
    - "a general framework for information extraction using dynamic span graphs"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "reading the manual event extraction as definition comprehension"):
  with members:
    - "entity relation extraction as multi turn question answering"
    - "matching the blanks distributional similarity for relation learning"
    - "one for all neural joint modeling of entities and events"
    - "know what you don t know unanswerable questions for squad"
    - "allennlp a deep semantic natural language processing platform"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a minimal span based neural constituency parser"
    - "zero shot relation extraction via reading comprehension"

Connected through hyperedge HE-6 (induced by source paper "doc2edag an end to end document level framework for chinese financial event extraction"):
  with members:
    - "one for all neural joint modeling of entities and events"
    - "fonduer knowledge base construction from richly formatted data"
    - "event extraction with generative adversarial imitation learning"
    - "reinforcement learning for relation classification from noisy data"
    - "diag nre a neural pattern diagnosis framework for distantly supervised neural relation extraction"
    - "attention is all you need"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "cotype joint extraction of typed entities and relations with knowledge bases"

Connected through hyperedge HE-7 (induced by source paper "document level n ary relation extraction with multiscale representation learning"):
  with members:
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "n ary relation extraction using graph state lstm"
    - "deep probabilistic logic a unifying framework for indirect supervision"
    - "fonduer knowledge base construction from richly formatted data"
    - "cross sentence n ary relation extraction with graph lstms"
    - "attention is all you need"
    - "distant supervision for relation extraction beyond the sentence boundary"
    - "hierarchical multiscale recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "open domain event extraction using neural latent variable models"):
  with members:
    - "jointly multiple events extraction via attention based graph information aggregation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint learning templates and slots for event schema induction"
    - "using sentence level lstm language models for script inference"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "probabilistic frame induction"

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)