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 "emotion recognition in conversation research challenges datasets and recent advances"):
  Abstract: Emotion is intrinsic to humans and consequently emotion understanding is a key part of human-like artificial intelligence (AI). Emotion recognition in conversation (ERC) is becoming increasingly popular as a new research frontier in natural language processing (NLP) due to its ability to mine opinions from the plethora of publicly available conversational data in platforms such as Facebook, Youtube, Reddit, Twitter, and others. Moreover, it has potential applications in health-care systems (as a tool for psychological analysis), education (understanding student frustration) and more. Additionally, ERC is also extremely important for generating emotion-aware dialogues that require an understanding of the user's emotions. Catering to these needs calls for effective and scalable conversational emotion-recognition algorithms. However, it is a strenuous problem to solve because of several research challenges. In this paper, we discuss these challenges and shed light on the recent research in this field. We also describe the drawbacks of these approaches and discuss the reasons why they fail to successfully overcome the research challenges in ERC.

Includes members:
  Member "snu_ids at semeval 2019 task 3 addressing training test class distribution mismatch in conversational classification":
    also connected through other hyperedges:
      (none)
  Member "ana at semeval 2019 task 3 contextual emotion detection in conversations through hierarchical lstms and bert":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bertscore evaluating text generation with bert")
  Member "meld a multimodal multi party dataset for emotion recognition in conversations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "knowledge enriched transformer for emotion detection in textual conversations")
  Member "emotionlines an emotion corpus of multi party conversations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "eda enriching emotional dialogue acts using an ensemble of neural annotators")
  Member "deep contextualized word representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "decision support with text based emotion recognition deep learning for affective computing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dialoguegcn a graph convolutional neural network for emotion recognition in conversation")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    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 "dialoguernn an attentive rnn for emotion detection in conversations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)