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: "effectiveness of anonymization in double blind review"
  Abstract: Double-blind review relies on the authors' ability and willingness to effectively anonymize their submissions. We explore anonymization effectiveness at ASE 2016, OOPSLA 2016, and PLDI 2016 by asking reviewers if they can guess author identities. We find that 74%-90% of reviews contain no correct guess and that reviewers who self-identify as experts on a paper's topic are more likely to attempt to guess, but no more likely to guess correctly. We present our findings, summarize the PC chairs' comments about administering double-blind review, discuss the advantages and disadvantages of revealing author identities part of the way through the process, and conclude by advocating for the continued use of double-blind review.

Connected through hyperedge HE-1 (induced by source paper "task guided pair embedding in heterogeneous network"):
  with members:
    - "link prediction based on graph neural networks"
    - "metagraph2vec complex semantic path augmented heterogeneous network embedding"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "aspem embedding learning by aspects in heterogeneous information networks"
    - "capturing edge attributes via network embedding"
    - "an attention based collaboration framework for multi view network representation learning"
    - "advances in pre training distributed word representations"
    - "heterogeneous information network embedding for recommendation"

Connected through hyperedge HE-2 (induced by source paper "prestige drives epistemic inequality in the diffusion of scientific ideas"):
  with members:
    - "the misleading narrative of the canonical faculty productivity trajectory"
    - "tracing the use of practices through networks of collaboration"
    - "gender productivity and prestige in computer science faculty hiring networks"
    - "can cascades be predicted"

Connected through hyperedge HE-3 (induced by source paper "a dataset of peer reviews peerread collection insights and nlp applications"):
  with members:
    - "content based citation recommendation"
    - "single versus double blind reviewing at wsdm 2017"

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.DL(Digital Libraries)