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: "jointly multiple events extraction via attention based graph information aggregation"
  Abstract: Event extraction is of practical utility in natural language processing. In the real world, it is a common phenomenon that multiple events existing in the same sentence, where extracting them are more difficult than extracting a single event. Previous works on modeling the associations between events by sequential modeling methods suffer a lot from the low efficiency in capturing very long-range dependencies. In this paper, we propose a novel Jointly Multiple Events Extraction (JMEE) framework to jointly extract multiple event triggers and arguments by introducing syntactic shortcut arcs to enhance information flow and attention-based graph convolution networks to model graph information. The experiment results demonstrate that our proposed framework achieves competitive results compared with state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "joint event extraction along shortest dependency paths using graph convolutional networks"):
  with members:
    - "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"
    - "joint extraction of events and entities within a document context"
    - "a compare aggregate model for matching text sequences"
    - "extracting temporal and causal relations between events"

Connected through hyperedge HE-2 (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"
    - "one for all neural joint modeling of entities and events"
    - "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-3 (induced by source paper "event detection with relation aware graph convolutional neural networks"):
  with members:
    - "attention guided graph convolutional networks for relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "dynamic routing between capsules"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "open domain event extraction using neural latent variable models"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint extraction of events and entities within a document context"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "incorporating fine grained events in stock movement prediction"):
  with members:
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "deep contextualized word representations"
    - "listening to chaotic whispers a deep learning framework for news oriented stock trend prediction"
    - "natural language inference by tree based convolution and heuristic matching"
    - "adam a method for stochastic optimization"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "cost sensitive regularization for label confusion aware event detection"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "deep contextualized word representations"
    - "nugget proposal networks for chinese event detection"
    - "event nugget detection with forward backward recurrent neural networks"
    - "adaptive scaling for sparse detection in information extraction"
    - "focal loss for dense object detection"

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)