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 "multi task learning with auxiliary speaker identification for conversational emotion recognition"):
  Abstract: Conversational emotion recognition (CER) has attracted increasing interests in the natural language processing (NLP) community. Different from the vanilla emotion recognition, effective speaker-sensitive utterance representation is one major challenge for CER. In this paper, we exploit speaker identification (SI) as an auxiliary task to enhance the utterance representation in conversations. By this method, we can learn better speaker-aware contextual representations from the additional SI corpus. Experiments on two benchmark datasets demonstrate that the proposed architecture is highly effective for CER, obtaining new state-of-the-art results on two datasets.

Includes members:
  Member "multi task networks with universe group and task feature learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi task learning network for emotion recognition in conversation")
  Member "knowledge enriched transformer for emotion detection in textual conversations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hierarchical transformer network for utterance level emotion recognition")
  Member "higru hierarchical gated recurrent units for utterance level emotion recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a unified system for aggression identification in english code mixed and uni lingual texts")
  Member "multi task learning with language modeling for question generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "asking questions the human way scalable question answer generation from text corpus")
  Member "dialoguegcn a graph convolutional neural network for emotion recognition in conversation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "emotion recognition in conversations with transfer learning from generative conversation modeling")
  Member "deep contextualized word representations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "end to end multimodal emotion recognition using deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi task learning network for emotion recognition in conversation")

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)