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: "cross device matching for online advertising with neural feature ensembles first place solution at cikm cup 2016"
  Abstract: We describe the 1st place winning approach for the CIKM Cup 2016 Challenge. In this paper, we provide an approach to reasonably identify same users across multiple devices based on browsing logs. Our approach regards a candidate ranking problem as pairwise classification and utilizes an unsupervised neural feature ensemble approach to learn latent features of users. Combined with traditional hand crafted features, each user pair feature is fed into a supervised classifier in order to perform pairwise classification. Lastly, we propose supervised and unsupervised inference techniques.

Connected through hyperedge HE-1 (induced by source paper "off policy evaluation of probabilistic identity data in lookalike modeling"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "siamese cookie embedding networks for cross device user matching"
    - "cross device user matching based on massive browse logs the runner up solution for the 2016 cikm cup"
    - "classification and learning to rank approaches for cross device matching at cikm cup 2016"
    - "doubly robust off policy value evaluation for reinforcement learning"
    - "cross device tracking matching devices and cookies"
    - "a flexible fitness function for community detection in complex networks"
    - "counterfactual reasoning and learning systems"

Connected through hyperedge HE-2 (induced by source paper "computationally inferred genealogical networks uncover long term trends in assortative mating"):
  with members:
    - "cross device user matching based on massive browse logs the runner up solution for the 2016 cikm cup"
    - "xgboost a scalable tree boosting system"
    - "application of advanced record linkage techniques for complex population reconstruction"
    - "active network alignment a matching based approach"

Connected through hyperedge HE-3 (induced by source paper "siamese cookie embedding networks for cross device user matching"):
  with members:
    - "contextual sequence modeling for recommendation with recurrent neural networks"
    - "wide deep learning for recommender systems"
    - "meta prod2vec product embeddings using side information for recommendation"
    - "document embedding with paragraph vectors"

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.AI(Artificial Intelligence)