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 "domain independent extraction of scientific concepts from research articles"):
  Abstract: We examine the novel task of domain-independent scientific concept extraction from abstracts of scholarly articles and present two contributions. First, we suggest a set of generic scientific concepts that have been identified in a systematic annotation process. This set of concepts is utilised to annotate a corpus of scientific abstracts from 10 domains of Science, Technology and Medicine at the phrasal level in a joint effort with domain experts. The resulting dataset is used in a set of benchmark experiments to (a) provide baseline performance for this task, (b) examine the transferability of concepts between domains. Second, we present two deep learning systems as baselines. In particular, we propose active learning to deal with different domains in our task. The experimental results show that (1) a substantial agreement is achievable by non-experts after consultation with domain experts, (2) the baseline system achieves a fairly high F1 score, (3) active learning enables us to nearly halve the amount of required training data.

Includes members:
  Member "structural scaffolds for citation intent classification in scientific publications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "citation text generation")
  Member "investigating correlations of inter coder agreement and machine annotation performance for historical video data":
    also connected through other hyperedges:
      (none)
  Member "hierarchical neural networks for sequential sentence classification in medical scientific abstracts":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pretrained language models for sequential sentence classification")
  Member "multi task identification of entities relations and coreference for scientific knowledge graph construction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deeper task specificity improves joint entity and relation extraction")
  Member "allennlp a deep semantic natural language processing platform":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "beat the ai investigating adversarial human annotations for reading comprehension")
  Member "construction of the literature graph in semantic scholar":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "navigation based candidate expansion and pretrained language models for citation recommendation")
  Member "deep bayesian active learning for natural language processing results of a large scale empirical study":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep active learning for efficient training of a lidar 3d object detector")
  Member "end to end neural coreference resolution":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi task learning based neural bridging reference resolution")

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.IR(Information Retrieval)