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: "neural tensor factorization"
  Abstract: Neural collaborative filtering (NCF) and recurrent recommender systems (RRN) have been successful in modeling user-item relational data. However, they are also limited in their assumption of static or sequential modeling of relational data as they do not account for evolving users' preference over time as well as changes in the underlying factors that drive the change in user-item relationship over time. We address these limitations by proposing a Neural Tensor Factorization (NTF) model for predictive tasks on dynamic relational data. The NTF model generalizes conventional tensor factorization from two perspectives: First, it leverages the long short-term memory architecture to characterize the multi-dimensional temporal interactions on relational data. Second, it incorporates the multi-layer perceptron structure for learning the non-linearities between different latent factors. Our extensive experiments demonstrate the significant improvement in rating prediction and link prediction on dynamic relational data by our NTF model over both neural network based factorization models and other traditional methods.

Connected through hyperedge HE-1 (induced by source paper "from persistent homology to reinforcement learning with applications for retail banking"):
  with members:
    - "non negative paratuck2 tensor decomposition combined to lstm network for smart contracts profiling"
    - "user device authentication in mobile banking using aphen for paratuck2 tensor decomposition"
    - "phom gem persistent homology for generative models"
    - "predicting sparse clients actions with cpopt net in the banking environment"
    - "shampoo preconditioned stochastic tensor optimization"
    - "implicit quantile networks for distributional reinforcement learning"
    - "a distributional perspective on reinforcement learning"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-2 (induced by source paper "a long short demands aware model for next item recommendation"):
  with members:
    - "sequence aware recommender systems"
    - "neural collaborative filtering"
    - "translation based recommendation"
    - "market basket prediction using user centric temporal annotated recurring sequences"
    - "scalable demand aware recommendation"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "neural attentive session based recommendation"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "water disaggregation via shape features based bayesian discriminative sparse coding"):
  with members:
    - "distributed self paced learning in alternating direction method of multipliers"
    - "open world knowledge graph completion"
    - "online and distributed robust regressions under adversarial data corruption"
    - "proje embedding projection for knowledge graph completion"
    - "large scale feature learning with spike and slab sparse coding"

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.LG(Machine Learning)