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: "scalable recommendation with poisson factorization"
  Abstract: We develop a Bayesian Poisson matrix factorization model for forming recommendations from sparse user behavior data. These data are large user/item matrices where each user has provided feedback on only a small subset of items, either explicitly (e.g., through star ratings) or implicitly (e.g., through views or purchases). In contrast to traditional matrix factorization approaches, Poisson factorization implicitly models each user's limited attention to consume items. Moreover, because of the mathematical form of the Poisson likelihood, the model needs only to explicitly consider the observed entries in the matrix, leading to both scalable computation and good predictive performance. We develop a variational inference algorithm for approximate posterior inference that scales up to massive data sets. This is an efficient algorithm that iterates over the observed entries and adjusts an approximate posterior over the user/item representations. We apply our method to large real-world user data containing users rating movies, users listening to songs, and users reading scientific papers. In all these settings, Bayesian Poisson factorization outperforms state-of-the-art matrix factorization methods.

Connected through hyperedge HE-1 (induced by source paper "joint neural collaborative filtering for recommender systems"):
  with members:
    - "neural graph collaborative filtering"
    - "deep learning based recommender system a survey and new perspectives"
    - "attention based hierarchical neural query suggestion"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "joint deep modeling of users and items using reviews for recommendation"

Connected through hyperedge HE-2 (induced by source paper "constructing high precision knowledge bases with subjective and factual attributes"):
  with members:
    - "subjective knowledge acquisition and enrichment powered by crowdsourcing"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "wide deep learning for recommender systems"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "learning distributed representations from reviews for collaborative filtering"
    - "collaborative deep learning for recommender systems"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-3 (induced by source paper "modeling and counteracting exposure bias in recommender systems"):
  with members:
    - "managing popularity bias in recommender systems with personalized re ranking"
    - "how algorithmic confounding in recommendation systems increases homogeneity and decreases utility"
    - "recommendations as treatments debiasing learning and evaluation"

Connected through hyperedge HE-4 (induced by source paper "exploring author gender in book rating and recommendation"):
  with members:
    - "beyond parity fairness objectives for collaborative filtering"
    - "multisided fairness for recommendation"
    - "runaway feedback loops in predictive policing"
    - "stereotypes in search engine results understanding the role of local and global factors"
    - "on the im possibility of fairness"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "image based recommendations on styles and substitutes"
    - "fairness through awareness"

Connected through hyperedge HE-5 (induced by source paper "collaborative item embedding model for implicit feedback data"):
  with members:
    - "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-6 (induced by source paper "recurrent neural network language models for open vocabulary event level cyber anomaly detection"):
  with members:
    - "deep learning for unsupervised insider threat detection in structured cybersecurity data streams"
    - "character level language modeling with hierarchical recurrent neural networks"
    - "character based neural machine translation"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "gated feedback recurrent neural networks"
    - "a clockwork rnn"

Connected through hyperedge HE-7 (induced by source paper "coupled compound poisson factorization"):
  with members:
    - "hierarchical compound poisson factorization"
    - "classification with low rank and missing data"
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"
    - "linear regression with limited observation"
    - "efficient learning with partially observed attributes"

Connected through hyperedge HE-8 (induced by source paper "collaborative filtering with user item co autoregressive models"):
  with members:
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "theano a python framework for fast computation of mathematical expressions"
    - "document neural autoregressive distribution estimation"
    - "deep residual learning for image recognition"
    - "neural network matrix factorization"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "neural machine translation by jointly learning to align and translate"

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.IR(Information Retrieval)