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 "discovering new intents via constrained deep adaptive clustering with cluster refinement"):
  Abstract: Identifying new user intents is an essential task in the dialogue system. However, it is hard to get satisfying clustering results since the definition of intents is strongly guided by prior knowledge. Existing methods incorporate prior knowledge by intensive feature engineering, which not only leads to overfitting but also makes it sensitive to the number of clusters. In this paper, we propose constrained deep adaptive clustering with cluster refinement (CDAC+), an end-to-end clustering method that can naturally incorporate pairwise constraints as prior knowledge to guide the clustering process. Moreover, we refine the clusters by forcing the model to learn from the high confidence assignments. After eliminating low confidence assignments, our approach is surprisingly insensitive to the number of clusters. Experimental results on the three benchmark datasets show that our method can yield significant improvements over strong baselines.

Includes members:
  Member "deep unknown intent detection with margin loss":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a post processing method for detecting unknown intent of dialogue system via pre trained deep neural network classifier")
  Member "train one get one free partially supervised neural network for bug report duplicate detection and clustering":
    also connected through other hyperedges:
      (none)
  Member "huggingface s transformers state of the art natural language processing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised question decomposition for question answering")
  Member "deep clustering for unsupervised learning of visual features":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning representations by predicting bags of visual words")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    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 "prototypical networks for few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "learning to cluster in order to transfer across domains and tasks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "m adda unsupervised domain adaptation with deep metric learning")
  Member "semi supervised clustering for short text via deep representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a deep learning based approach for measuring the domain similarity of persian texts")

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)