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 "off policy evaluation of probabilistic identity data in lookalike modeling"):
  Abstract: We evaluate the impact of probabilistically-constructed digital identity data collected between Sep. 2017 and Dec. 2017, approximately, in the context of Lookalike-targeted campaigns. The backbone of this study is a large set of probabilistically-constructed "identities", represented as small bags of cookies and mobile ad identifiers with associated metadata, that are likely all owned by the same underlying user. The identity data allows to generate "identity-based", rather than "identifier-based", user models, giving a fuller picture of the interests of the users underlying the identifiers. We employ off-policy evaluation techniques to evaluate the potential of identity-powered lookalike models without incurring the risk of allowing untested models to direct large amounts of ad spend or the large cost of performing A/B tests. We add to historical work on off-policy evaluation by noting a significant type of "finite-sample bias" that occurs for studies combining modestly-sized datasets and evaluation metrics based on ratios involving rare events (e.g., conversions). We illustrate this bias using a simulation study that later informs the handling of inverse propensity weights in our analyses on real data. We demonstrate significant lift in identity-powered lookalikes versus an identity-ignorant baseline: on average ~70% lift in conversion rate, CVR, with a concordant drop in cost-per-acquisition, CPA. This rises to factors of ~(4-32)x for identifiers having little data themselves, but that can be inferred to belong to users with substantial data to aggregate across identifiers. This implies that identity-powered user modeling is especially important in the context of identifiers having very short lifespans (i.e., frequently churned cookies). Our work motivates and informs the use of probabilistically-constructed digital identities in the marketing context. It also deepens the canon of examples in which off-policy learning has been employed to evaluate the complex systems of the internet economy.

Includes members:
  Member "graph embedding techniques applications and performance a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "siamese cookie embedding networks for cross device user matching":
    also connected through other hyperedges:
      (none)
  Member "cross device user matching based on massive browse logs the runner up solution for the 2016 cikm cup":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive factorization network learning adaptive order feature interactions")
  Member "cross device matching for online advertising with neural feature ensembles first place solution at cikm cup 2016":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "computationally inferred genealogical networks uncover long term trends in assortative mating")
  Member "classification and learning to rank approaches for cross device matching at cikm cup 2016":
    also connected through other hyperedges:
      (none)
  Member "doubly robust off policy value evaluation for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reinforcement learning via fenchel rockafellar duality")
  Member "cross device tracking matching devices and cookies":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "talon an automated framework for cross device tracking detection")
  Member "a flexible fitness function for community detection in complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "metrics for community analysis a survey")

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.LG(Machine Learning)