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 "learning to contextually aggregate multi source supervision for sequence labeling"):
  Abstract: Sequence labeling is a fundamental framework for various natural language processing problems. Its performance is largely influenced by the annotation quality and quantity in supervised learning scenarios. In many cases, ground truth labels are costly and time-consuming to collect or even non-existent, while imperfect ones could be easily accessed or transferred from different domains. In this paper, we propose a novel framework named consensus Network (ConNet) to conduct training with imperfect annotations from multiple sources. It learns the representation for every weak supervision source and dynamically aggregates them by a context-aware attention mechanism. Finally, it leads to a model reflecting the consensus among multiple sources. We evaluate the proposed framework in two practical settings of multisource learning: learning with crowd annotations and unsupervised cross-domain model adaptation. Extensive experimental results show that our model achieves significant improvements over existing methods in both settings.

Includes members:
  Member "crossweigh training named entity tagger from imperfect annotations":
    also connected through other hyperedges:
      (none)
  Member "massively multilingual transfer for ner":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distant supervision and noisy label learning for low resource named entity recognition a study on hausa and yor ub a")
  Member "multinomial adversarial networks for multi domain text classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a label proportions estimation technique for adversarial domain adaptation in text classification")
  Member "strong baselines for neural semi supervised learning under domain shift":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pseudo labeling and negative feedback learning for large scale multi label domain classification")
  Member "cross type biomedical named entity recognition with deep multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mt bioner multi task learning for biomedical named entity recognition using deep bidirectional transformers")
  Member "semi supervised sequence modeling with cross view training":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "snippext semi supervised opinion mining with augmented data")
  Member "neural adaptation layers for cross domain named entity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "zero resource cross domain named entity recognition")
  Member "asymmetric tri training for unsupervised domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsupervised domain adaptive object detection using forward backward cyclic adaptation")

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)