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 "a unified system for aggression identification in english code mixed and uni lingual texts"):
  Abstract: Wide usage of social media platforms has increased the risk of aggression, which results in mental stress and affects the lives of people negatively like psychological agony, fighting behavior, and disrespect to others. Majority of such conversations contains code-mixed languages[28]. Additionally, the way used to express thought or communication style also changes from one social media plat-form to another platform (e.g., communication styles are different in twitter and Facebook). These all have increased the complexity of the problem. To solve these problems, we have introduced a unified and robust multi-modal deep learning architecture which works for English code-mixed dataset and uni-lingual English dataset both.The devised system, uses psycho-linguistic features and very ba-sic linguistic features. Our multi-modal deep learning architecture contains, Deep Pyramid CNN, Pooled BiLSTM, and Disconnected RNN(with Glove and FastText embedding, both). Finally, the system takes the decision based on model averaging. We evaluated our system on English Code-Mixed TRAC 2018 dataset and uni-lingual English dataset obtained from Kaggle. Experimental results show that our proposed system outperforms all the previous approaches on English code-mixed dataset and uni-lingual English dataset.

Includes members:
  Member "hierarchical taxonomy aware and attentional graph capsule rcnns for large scale multi label text classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "aggressionnet generalised multi modal deep temporal and sequential learning for aggression identification")
  Member "higru hierarchical gated recurrent units for utterance level emotion recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "aggressionnet generalised multi modal deep temporal and sequential learning for aggression identification")
  Member "towards generating long and coherent text with multi level latent variable models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "aggressionnet generalised multi modal deep temporal and sequential learning for aggression identification")
  Member "mattnet modular attention network for referring expression comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "universal language model fine tuning for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "description based text classification with reinforcement learning")
  Member "lstms with attention for aggression detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "aggressionnet generalised multi modal deep temporal and sequential learning for aggression identification")
  Member "ritual uh at trac 2018 shared task aggression identification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "aggressionnet generalised multi modal deep temporal and sequential learning for aggression identification")
  Member "aggression annotated corpus of hindi english code mixed data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "aggressionnet generalised multi modal deep temporal and sequential learning for aggression identification")

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)