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 "related tasks can share a multi task framework for affective language"):
  Abstract: Expressing the polarity of sentiment as 'positive' and 'negative' usually have limited scope compared with the intensity/degree of polarity. These two tasks (i.e. sentiment classification and sentiment intensity prediction) are closely related and may offer assistance to each other during the learning process. In this paper, we propose to leverage the relatedness of multiple tasks in a multi-task learning framework. Our multi-task model is based on convolutional-Gated Recurrent Unit (GRU) framework, which is further assisted by a diverse hand-crafted feature set. Evaluation and analysis suggest that joint-learning of the related tasks in a multi-task framework can outperform each of the individual tasks in the single-task frameworks.

Includes members:
  Member "semeval 2016 task 4 sentiment analysis in twitter":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "supervised phrase boundary embeddings")
  Member "semeval 2017 task 4 sentiment analysis in twitter":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised sentiment analysis for code mixed data")
  Member "amobee at semeval 2018 task 1 gru neural network with a cnn attention mechanism for sentiment classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gated recurrent neural network approach for multilabel emotion detection in microblogs")
  Member "plusemo2vec at semeval 2018 task 1 exploiting emotion knowledge from emoji and hashtags":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "clickbait sensational headline generation with auto tuned reinforcement learning")
  Member "learning to generate reviews and discovering sentiment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to generate multiple style transfer outputs for an input sentence")
  Member "seernet at semeval 2018 task 1 domain adaptation for affect in tweets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "affect in tweets using experts model")
  Member "wassa 2017 shared task on emotion intensity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "establishing human robot trust through music driven robotic emotion prosody and gesture")
  Member "an overview of multi task learning in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

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)