human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "global locality in event extraction"):
  Abstract: Due to the exponential growth of biomedical literature, event and relation extraction are important tasks in biomedical text mining. Most work in relation extraction detect a single entity pair mention on a short span of text, which is not ideal due to long sentences that appear in biomedical contexts. We propose an approach to both event and relation extraction, for simultaneously predicting relationships between all mention pairs in a text. Our model includes a set of multi-head attentions and convolutions, an adaptation of the transformer architecture, which offers self-attention the ability to strengthen dependencies among related elements, and models the interaction between features extracted by multiple attention heads. Experiment results demonstrate that our approach outperforms the state-of-the-art on a set of benchmark biomedical corpora including BioNLP 2009, 2011, 2013 and BioCreative 2017 shared tasks.

Includes members:
  Member "simultaneously self attending to all mentions for full abstract biological relation extraction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "leveraging dependency forest for neural medical relation extraction")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "a structured self attentive sentence embedding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "high order paired aspp networks for semantic segmenation")
  Member "disan directional self attention network for rnn cnn free language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "high order paired aspp networks for semantic segmenation")
  Member "a decomposable attention model for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a 3 accelerating attention mechanisms in neural networks with approximation")
  Member "multilingual relation extraction using compositional universal schema":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "zero shot cross lingual transfer with meta learning")
  Member "classifying relations by ranking with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "autoextend extending word embeddings to embeddings for synsets and lexemes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using word embeddings to improve the discriminability of co occurrence text networks")

Question: Please tell me which class the source paper 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)