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 user matching based on massive browse logs the runner up solution for the 2016 cikm cup"
  Abstract: As the number and variety of smart devices increase, users may use myriad devices in their daily lives and the online activities become highly fragmented. Building an accurate user identity becomes a difficult and important problem for advertising companies. The task for the CIKM Cup 2016 Track 1 was to find the same user cross multiple devices. This paper discusses our solution to the challenge. It is mainly comprised of three parts: comprehensive feature engineering, negative sampling, and model selection. For each part we describe our special steps and demonstrate how the performance is boosted. We took the second prize of the competition with an F1-score of 0.41669.

Connected through hyperedge HE-1 (induced by source paper "adaptive factorization network learning adaptive order feature interactions"):
  with members:
    - "autoint automatic feature interaction learning via self attentive neural networks"
    - "product based neural networks for user response prediction over multi field categorical data"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "neural factorization machines for sparse predictive analytics"
    - "deep cross network for ad click predictions"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"
    - "wide deep learning for recommender systems"

Connected through hyperedge HE-2 (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 matching for online advertising with neural feature ensembles first place solution at cikm cup 2016"
    - "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-3 (induced by source paper "xdeepfm combining explicit and implicit feature interactions for recommender systems"):
  with members:
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "deep cross network for ad click predictions"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"
    - "wide deep learning for recommender systems"
    - "product based neural networks for user response prediction"
    - "vbpr visual bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-4 (induced by source paper "computationally inferred genealogical networks uncover long term trends in assortative mating"):
  with members:
    - "xgboost a scalable tree boosting system"
    - "application of advanced record linkage techniques for complex population reconstruction"
    - "active network alignment a matching based approach"
    - "cross device matching for online advertising with neural feature ensembles first place solution at cikm cup 2016"

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.SI(Social and Information Networks)