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: "estimating the causal impact of recommendation systems from observational data"
  Abstract: Recommendation systems are an increasingly prominent part of the web, accounting for up to a third of all traffic on several of the world's most popular sites. Nevertheless, little is known about how much activity such systems actually cause over and above activity that would have occurred via other means (e.g., search) if recommendations were absent. Although the ideal way to estimate the causal impact of recommendations is via randomized experiments, such experiments are costly and may inconvenience users. In this paper, therefore, we present a method for estimating causal effects from purely observational data. Specifically, we show that causal identification through an instrumental variable is possible when a product experiences an instantaneous shock in direct traffic and the products recommended next to it do not. We then apply our method to browsing logs containing anonymized activity for 2.1 million users on Amazon.com over a 9 month period and analyze over 4,000 unique products that experience such shocks. We find that although recommendation click-throughs do account for a large fraction of traffic among these products, at least 75% of this activity would likely occur in the absence of recommendations. We conclude with a discussion about the assumptions under which the method is appropriate and caveats around extrapolating results to other products, sites, or settings.

Connected through hyperedge HE-1 (induced by source paper "efficiently learning and sampling interventional distributions from observations"):
  with members:
    - "efficient distance approximation for structured high dimensional distributions via learning"
    - "learning and testing causal models with interventions"
    - "identifiability and transportability in dynamic causal networks"
    - "testing bayesian networks"
    - "axiomatizing causal reasoning"
    - "testing identifiability of causal effects"
    - "causal networks semantics and expressiveness"
    - "probabilistic evaluation of sequential plans from causal models with hidden variables"

Connected through hyperedge HE-2 (induced by source paper "modeling embedding dimension correlations via convolutional neural collaborative filtering"):
  with members:
    - "temporal relational ranking for stock prediction"
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "fast matrix factorization with non uniform weights on missing data"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "product based neural networks for user response prediction over multi field categorical data"
    - "outer product based neural collaborative filtering"
    - "adversarial personalized ranking for recommendation"
    - "a simple convolutional generative network for next item recommendation"

Connected through hyperedge HE-3 (induced by source paper "estimating attention flow in online video networks"):
  with members:
    - "offline evaluation of ranking policies with click models"
    - "top k off policy correction for a reinforce recommender system"
    - "sir hawkes linking epidemic models and hawkes processes to model diffusions in finite populations"
    - "foundations of sequence to sequence modeling for time series"
    - "beyond views measuring and predicting engagement on youtube videos"
    - "online popularity under promotion viral potential forecasting and the economics of time"
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "do cascades recur"

Connected through hyperedge HE-4 (induced by source paper "adaptive sequential experiments with unknown information flows"):
  with members:
    - "meta dynamic pricing learning across experiments"
    - "bandit learning with positive externalities"
    - "optimal exploration exploitation in a multi armed bandit problem with non stationary rewards"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "real time bidding benchmarking with ipinyou dataset"
    - "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-5 (induced by source paper "how algorithmic confounding in recommendation systems increases homogeneity and decreases utility"):
  with members:
    - "recommendations as treatments debiasing learning and evaluation"
    - "modeling mutual feedback between users and recommender systems"
    - "a probabilistic model to resolve diversity accuracy challenge of recommendation systems"
    - "reducing offline evaluation bias in recommendation systems"
    - "discrimination in online ad delivery"
    - "biased assimilation homophily and the dynamics of polarization"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"
    - "solving the apparent diversity accuracy dilemma of recommender systems"

Connected through hyperedge HE-6 (induced by source paper "evolution of ego networks in social media with link recommendations"):
  with members:
    - "averaging gone wrong using time aware analyses to better understand behavior"
    - "gender and interest targeting for sponsored post advertising at tumblr"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "what is tumblr a statistical overview and comparison"
    - "structural properties of ego networks"
    - "discovering social circles in ego networks"
    - "the anatomy of the facebook social graph"
    - "fast incremental and personalized pagerank"

Connected through hyperedge HE-7 (induced by source paper "the limits of popularity based recommendations and the role of social ties"):
  with members:
    - "modeling mutual feedback between users and recommender systems"
    - "do social explanations work studying and modeling the effects of social explanations in recommender systems"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

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)