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 "the stem ecr dataset grounding scientific entity references in stem scholarly content to authoritative encyclopedic and lexicographic sources"):
  Abstract: We introduce the STEM (Science, Technology, Engineering, and Medicine) Dataset for Scientific Entity Extraction, Classification, and Resolution, version 1.0 (STEM-ECR v1.0). The STEM-ECR v1.0 dataset has been developed to provide a benchmark for the evaluation of scientific entity extraction, classification, and resolution tasks in a domain-independent fashion. It comprises abstracts in 10 STEM disciplines that were found to be the most prolific ones on a major publishing platform. We describe the creation of such a multidisciplinary corpus and highlight the obtained findings in terms of the following features: 1) a generic conceptual formalism for scientific entities in a multidisciplinary scientific context; 2) the feasibility of the domain-independent human annotation of scientific entities under such a generic formalism; 3) a performance benchmark obtainable for automatic extraction of multidisciplinary scientific entities using BERT-based neural models; 4) a delineated 3-step entity resolution procedure for human annotation of the scientific entities via encyclopedic entity linking and lexicographic word sense disambiguation; and 5) human evaluations of Babelfy returned encyclopedic links and lexicographic senses for our entities. Our findings cumulatively indicate that human annotation and automatic learning of multidisciplinary scientific concepts as well as their semantic disambiguation in a wide-ranging setting as STEM is reasonable.

Includes members:
  Member "scibert a pretrained language model for scientific text":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "snippext semi supervised opinion mining with augmented data")
  Member "open research knowledge graph next generation infrastructure for semantic scholarly knowledge":
    also connected through other hyperedges:
      (none)
  Member "multi task identification of entities relations and coreference for scientific knowledge graph construction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "beat the ai investigating adversarial human annotations for reading comprehension")
  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 "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 "semeval 2017 task 10 scienceie extracting keyphrases and relations from scientific publications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "domain independent extraction of scientific concepts from research articles")
  Member "scientific information extraction with semi supervised neural tagging":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neural keyphrase generation via reinforcement learning with adaptive rewards")

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)