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: "ordinal boltzmann machines for collaborative filtering"
  Abstract: Collaborative filtering is an effective recommendation technique wherein the preference of an individual can potentially be predicted based on preferences of other members. Early algorithms often relied on the strong locality in the preference data, that is, it is enough to predict preference of a user on a particular item based on a small subset of other users with similar tastes or of other items with similar properties. More recently, dimensionality reduction techniques have proved to be equally competitive, and these are based on the co-occurrence patterns rather than locality. This paper explores and extends a probabilistic model known as Boltzmann Machine for collaborative filtering tasks. It seamlessly integrates both the similarity and co-occurrence in a principled manner. In particular, we study parameterisation options to deal with the ordinal nature of the preferences, and propose a joint modelling of both the user-based and item-based processes. Experiments on moderate and large-scale movie recommendation show that our framework rivals existing well-known 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 "neural collaborative filtering"):
  with members:
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "neural autoregressive collaborative filtering for implicit feedback"
    - "wide deep learning for recommender systems"
    - "a neural autoregressive approach to collaborative filtering"
    - "a generic coordinate descent framework for learning from implicit feedback"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "collaborative deep learning for recommender systems"

Connected through hyperedge HE-3 (induced by source paper "hybrid recommender system based on autoencoders"):
  with members:
    - "a network based end to end trainable task oriented dialogue system"
    - "wide deep learning for recommender systems"
    - "collaborative recurrent autoencoder recommend while learning to fill in the blanks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "neural network matrix factorization"
    - "adam a method for stochastic optimization"
    - "collaborative deep learning for recommender systems"

Connected through hyperedge HE-4 (induced by source paper "a neural autoregressive approach to collaborative filtering"):
  with members:
    - "a deep and autoregressive approach for topic modeling of multimodal data"
    - "blocks and fuel frameworks for deep learning"
    - "deep residual learning for image recognition"
    - "neural network matrix factorization"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "scalable recommendation with poisson factorization"
    - "theano new features and speed improvements"

Connected through hyperedge HE-5 (induced by source paper "hybrid collaborative filtering with autoencoders"):
  with members:
    - "hybrid collaborative filtering with neural networks"
    - "neural network matrix factorization"
    - "collaborative deep learning for recommender systems"
    - "conditional random field autoencoders for unsupervised structured prediction"
    - "a comparative study of collaborative filtering algorithms"

Connected through hyperedge HE-6 (induced by source paper "multi domain collaborative filtering"):
  with members:
    - "dependency networks for collaborative filtering and data visualization"
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "probabilistic models for unified collaborative and content based recommendation in sparse data environments"
    - "collaborative filtering by personality diagnosis a hybrid memory and model based approach"
    - "a bayesian approach toward active learning for collaborative filtering"
    - "active collaborative filtering"
    - "preference based graphic models for collaborative filtering"

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)