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: "zero shot transfer learning for event extraction"
  Abstract: Most previous event extraction studies have relied heavily on features derived from annotated event mentions, thus cannot be applied to new event types without annotation effort. In this work, we take a fresh look at event extraction and model it as a grounding problem. We design a transferable neural architecture, mapping event mentions and types jointly into a shared semantic space using structural and compositional neural networks, where the type of each event mention can be determined by the closest of all candidate types . By leveraging (1)~available manual annotations for a small set of existing event types and (2)~existing event ontologies, our framework applies to new event types without requiring additional annotation. Experiments on both existing event types (e.g., ACE, ERE) and new event types (e.g., FrameNet) demonstrate the effectiveness of our approach. \textit{Without any manual annotations} for 23 new event types, our zero-shot framework achieved performance comparable to a state-of-the-art supervised model which is trained from the annotations of 500 event mentions.

Connected through hyperedge HE-1 (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-2 (induced by source paper "zero shot adaptive transfer for conversational language understanding"):
  with members:
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "fast and scalable expansion of natural language understanding functionality for intelligent agents"
    - "end to end neural coreference resolution"
    - "domain adaptation of recurrent neural networks for natural language understanding"
    - "joint online spoken language understanding and language modeling with recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-3 (induced by source paper "one for all neural joint modeling of entities and events"):
  with members:
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-4 (induced by source paper "event representations with tensor based compositions"):
  with members:
    - "distributed representations of sentences and documents"
    - "a study of entanglement in a categorical framework of natural language"
    - "evaluating neural word representations in tensor based compositional settings"
    - "learning semantic script knowledge with event embeddings"
    - "probabilistic frame induction"
    - "multi step regression learning for compositional distributional semantics"

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)