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: "real time bidding benchmarking with ipinyou dataset"
  Abstract: Being an emerging paradigm for display advertising, Real-Time Bidding (RTB) drives the focus of the bidding strategy from context to users' interest by computing a bid for each impression in real time. The data mining work and particularly the bidding strategy development becomes crucial in this performance-driven business. However, researchers in computational advertising area have been suffering from lack of publicly available benchmark datasets, which are essential to compare different algorithms and systems. Fortunately, a leading Chinese advertising technology company iPinYou decided to release the dataset used in its global RTB algorithm competition in 2013. The dataset includes logs of ad auctions, bids, impressions, clicks, and final conversions. These logs reflect the market environment as well as form a complete path of users' responses from advertisers' perspective. This dataset directly supports the experiments of some important research problems such as bid optimisation and CTR estimation. To the best of our knowledge, this is the first publicly available dataset on RTB display advertising. Thus, they are valuable for reproducible research and understanding the whole RTB ecosystem. In this paper, we first provide the detailed statistical analysis of this dataset. Then we introduce the research problem of bid optimisation in RTB and the simple yet comprehensive evaluation protocol. Besides, a series of benchmark experiments are also conducted, including both click-through rate (CTR) estimation and bid optimisation.

Connected through hyperedge HE-1 (induced by source paper "scalable bid landscape forecasting in real time bidding"):
  with members:
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "machine learning for survival analysis a survey"
    - "deep cross network for ad click predictions"
    - "display advertising with real time bidding rtb and behavioural targeting"
    - "adam a method for stochastic optimization"
    - "real time bidding for online advertising measurement and analysis"

Connected through hyperedge HE-2 (induced by source paper "deep landscape forecasting for real time bidding advertising"):
  with members:
    - "bidding machine learning to bid for directly optimizing profits in display advertising"
    - "sliced recurrent neural networks"
    - "deep learning for patient specific kidney graft survival analysis"
    - "display advertising with real time bidding rtb and behavioural targeting"
    - "product based neural networks for user response prediction"
    - "architectural complexity measures of recurrent neural networks"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "real time bidding for online advertising measurement and analysis"

Connected through hyperedge HE-3 (induced by source paper "zorro a model agnostic system to price consumer data"):
  with members:
    - "a marketplace for data an algorithmic solution"
    - "display advertising with real time bidding rtb and behavioural targeting"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "tensorflow a system for large scale machine learning"
    - "mllib machine learning in apache spark"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-4 (induced by source paper "learning continuous user representations through hybrid filtering with doc2vec"):
  with members:
    - "item2vec neural item embedding for collaborative filtering"
    - "display advertising with real time bidding rtb and behavioural targeting"
    - "from word embeddings to item recommendation"
    - "a flexible recommendation system for cable tv"
    - "skip thought vectors"
    - "e commerce in your inbox product recommendations at scale"
    - "distributed representations of sentences and documents"
    - "show and tell a neural image caption generator"

Connected through hyperedge HE-5 (induced by source paper "adaptive sequential experiments with unknown information flows"):
  with members:
    - "meta dynamic pricing learning across experiments"
    - "bandit learning with positive externalities"
    - "estimating the causal impact of recommendation systems from observational data"
    - "optimal exploration exploitation in a multi armed bandit problem with non stationary rewards"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "real time bid optimization with smooth budget delivery in online advertising"
    - "further optimal regret bounds for thompson sampling"

Connected through hyperedge HE-6 (induced by source paper "the second price knapsack problem near optimal real time bidding in internet advertisement"):
  with members:
    - "bidding machine learning to bid for directly optimizing profits in display advertising"
    - "multiplicative pacing equilibria in auction markets"
    - "fast algorithms for online stochastic convex programming"
    - "bandits with knapsacks"
    - "real time bidding for online advertising measurement and analysis"
    - "real time bid optimization with smooth budget delivery in online advertising"
    - "online stochastic packing applied to display ad allocation"
    - "a dynamic near optimal algorithm for online linear programming"

Connected through hyperedge HE-7 (induced by source paper "neural feature embedding for user response prediction in real time bidding rtb"):
  with members:
    - "deep learning over multi field categorical data a case study on user response prediction"
    - "display advertising with real time bidding rtb and behavioural targeting"
    - "implicit look alike modelling in display ads transfer collaborative filtering to ctr estimation"
    - "tensorflow a system for large scale machine learning"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "display advertising with real time bidding rtb and behavioural targeting"):
  with members:
    - "neural feature embedding for user response prediction in real time bidding rtb"
    - "optimal real time bidding frameworks discussion"
    - "product based neural networks for user response prediction"
    - "lift based bidding in ad selection"
    - "statistical arbitrage mining for display advertising"
    - "an empirical study on display ad impression viewability measurements"
    - "neural machine translation by jointly learning to align and translate"
    - "real time bid optimization with smooth budget delivery in online advertising"

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.GT(Computer Science and Game Theory)