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: "discrete factorization machines for fast feature based recommendation"
  Abstract: User and item features of side information are crucial for accurate recommendation. However, the large number of feature dimensions, e.g., usually larger than 10^7, results in expensive storage and computational cost. This prohibits fast recommendation especially on mobile applications where the computational resource is very limited. In this paper, we develop a generic feature-based recommendation model, called Discrete Factorization Machine (DFM), for fast and accurate recommendation. DFM binarizes the real-valued model parameters (e.g., float32) of every feature embedding into binary codes (e.g., boolean), and thus supports efficient storage and fast user-item score computation. To avoid the severe quantization loss of the binarization, we propose a convergent updating rule that resolves the challenging discrete optimization of DFM. Through extensive experiments on two real-world datasets, we show that 1) DFM consistently outperforms state-of-the-art binarized recommendation models, and 2) DFM shows very competitive performance compared to its real-valued version (FM), demonstrating the minimized quantization loss. This work is accepted by IJCAI 2018.

Connected through hyperedge HE-1 (induced by source paper "candidate generation with binary codes for large scale top n recommendation"):
  with members:
    - "ranking distillation learning compact ranking models with high performance for recommender system"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "deep discrete supervised hashing"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "the case for learned index structures"
    - "neural collaborative filtering"
    - "translation based recommendation"
    - "asymmetric deep supervised hashing"

Connected through hyperedge HE-2 (induced by source paper "compositional coding for collaborative filtering"):
  with members:
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "hash embeddings for efficient word representations"
    - "collaborative filtering with social local models"
    - "learning k way d dimensional discrete code for compact embedding representations"
    - "compressing word embeddings via deep compositional code learning"
    - "neural attentive session based recommendation"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-3 (induced by source paper "hierarchical attention network for visually aware food recommendation"):
  with members:
    - "adversarial personalized ranking for recommendation"
    - "aesthetic based clothing recommendation"
    - "grab pay and eat semantic food detection for smart restaurants"
    - "neural collaborative filtering"
    - "item silk road recommending items from information domains to social users"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"
    - "wide slice residual networks for food recognition"

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)