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: "one for all neural joint modeling of entities and events"
  Abstract: The previous work for event extraction has mainly focused on the predictions for event triggers and argument roles, treating entity mentions as being provided by human annotators. This is unrealistic as entity mentions are usually predicted by some existing toolkits whose errors might be propagated to the event trigger and argument role recognition. Few of the recent work has addressed this problem by jointly predicting entity mentions, event triggers and arguments. However, such work is limited to using discrete engineering features to represent contextual information for the individual tasks and their interactions. In this work, we propose a novel model to jointly perform predictions for entity mentions, event triggers and arguments based on the shared hidden representations from deep learning. The experiments demonstrate the benefits of the proposed method, leading to the state-of-the-art performance for event extraction.

Connected through hyperedge HE-1 (induced by source paper "training with streaming annotation"):
  with members:
    - "learning personas from dialogue with attentive memory networks"
    - "exploiting contextual information via dynamic memory network for event detection"
    - "jointly multiple events extraction via attention based graph information aggregation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "long term memory networks for question answering"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "end to end memory networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "exploiting the matching information in the support set for few shot event classification"):
  with members:
    - "extending event detection to new types with learning from keywords"
    - "diverse few shot text classification with multiple metrics"
    - "attention is all you need"
    - "prototypical networks for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "matching networks for one shot learning"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (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"
    - "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-4 (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"
    - "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"
    - "zero shot transfer learning for event extraction"

Connected through hyperedge HE-5 (induced by source paper "doc2edag an end to end document level framework for chinese financial event extraction"):
  with members:
    - "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"
    - "joint extraction of events and entities within a document context"
    - "cotype joint extraction of typed entities and relations with knowledge bases"

Connected through hyperedge HE-6 (induced by source paper "graph based neural networks for event factuality prediction using syntactic and semantic structures"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural models of factuality"
    - "exploring different dimensions of attention for uncertainty detection"
    - "multilingual modal sense classification using a convolutional neural network"
    - "toward mention detection robustness with recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "extending event detection to new types with learning from keywords"):
  with members:
    - "who is killed by police introducing supervised attention for hierarchical lstms"
    - "film visual reasoning with a general conditioning layer"
    - "neural machine translation by jointly learning to align and translate"
    - "distributed representations of words and phrases and their compositionality"

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)