human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "crowdsourcing paper screening in systematic literature reviews"
  Abstract: Literature reviews allow scientists to stand on the shoulders of giants, showing promising directions, summarizing progress, and pointing out existing challenges in research. At the same time conducting a systematic literature review is a laborious and consequently expensive process. In the last decade, there have a few studies on crowdsourcing in literature reviews. This paper explores the feasibility of crowdsourcing for facilitating the literature review process in terms of results, time and effort, as well as to identify which crowdsourcing strategies provide the best results based on the budget available. In particular we focus on the screening phase of the literature review process and we contribute and assess methods for identifying the size of tests, labels required per paper, and classification functions as well as methods to split the crowdsourcing process in phases to improve results. Finally, we present our findings based on experiments run on Crowdflower.

Connected through hyperedge HE-1 (induced by source paper "combining crowd and machines for multi predicate item screening"):
  with members:
    - "crowd based multi predicate screening of papers in literature reviews"
    - "optimality of belief propagation for crowdsourced classification"
    - "crowdsourcing information extraction for biomedical systematic reviews"
    - "data fusion resolving conflicts from multiple sources"
    - "crowd access path optimization diversity matters"
    - "truelabel confusions a spectrum of probabilistic models in analyzing multiple ratings"
    - "crowder crowdsourcing entity resolution"

Connected through hyperedge HE-2 (induced by source paper "understanding the impact of text highlighting in crowdsourcing tasks"):
  with members:
    - "fine tune bert for extractive summarization"
    - "combining crowd and machines for multi predicate item screening"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "coqa a conversational question answering challenge"
    - "crowdrev a platform for crowd based screening of literature reviews"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "crowdrev a platform for crowd based screening of literature reviews"):
  with members:
    - "crowd based multi predicate screening of papers in literature reviews"
    - "crowdsourcing information extraction for biomedical systematic reviews"
    - "truelabel confusions a spectrum of probabilistic models in analyzing multiple ratings"

Connected through hyperedge HE-4 (induced by source paper "crowd based multi predicate screening of papers in literature reviews"):
  with members:
    - "crowdsourcing information extraction for biomedical systematic reviews"
    - "truelabel confusions a spectrum of probabilistic models in analyzing multiple ratings"

Connected through hyperedge HE-5 (induced by source paper "crowd machine collaboration for item screening"):
  with members:
    - "crowd based multi predicate screening of papers in literature reviews"

Question: Please tell me which class the center node 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)