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 information extraction for biomedical systematic reviews"
  Abstract: Information extraction is a critical step in the practice of conducting biomedical systematic literature reviews. Extracted structured data can be aggregated via methods such as statistical meta-analysis. Typically highly trained domain experts extract data for systematic reviews. The high expense of conducting biomedical systematic reviews has motivated researchers to explore lower cost methods that achieve similar rigor without compromising quality. Crowdsourcing represents one such promising approach. In this work-in-progress study, we designed a crowdsourcing task for biomedical information extraction. We briefly report the iterative design process and the results of two pilot testings. We found that giving more concrete examples in the task instruction can help workers better understand the task, especially for concepts that are abstract and confusing. We found a few workers completed most of the work, and our payment level appeared more attractive to workers from low-income countries. In the future, we will further evaluate our results with reference to gold standard extractions, thus assessing the feasibility of tasking crowd workers with extracting biomedical intervention information for systematic reviews.

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"
    - "crowdsourcing paper screening in systematic literature reviews"
    - "optimality of belief propagation for crowdsourced classification"
    - "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 "crowdrev a platform for crowd based screening of literature reviews"):
  with members:
    - "crowd based multi predicate screening of papers in literature reviews"
    - "crowdsourcing paper screening in systematic literature reviews"
    - "truelabel confusions a spectrum of probabilistic models in analyzing multiple ratings"

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

Connected through hyperedge HE-4 (induced by source paper "crowdsourcing paper screening in systematic literature reviews"):
  with members:
    - "truelabel confusions a spectrum of probabilistic models in analyzing multiple ratings"

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.HC(Human-Computer Interaction)