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: "outer product based neural collaborative filtering"
  Abstract: In this work, we contribute a new multi-layer neural network architecture named ONCF to perform collaborative filtering. The idea is to use an outer product to explicitly model the pairwise correlations between the dimensions of the embedding space. In contrast to existing neural recommender models that combine user embedding and item embedding via a simple concatenation or element-wise product, our proposal of using outer product above the embedding layer results in a two-dimensional interaction map that is more expressive and semantically plausible. Above the interaction map obtained by outer product, we propose to employ a convolutional neural network to learn high-order correlations among embedding dimensions. Extensive experiments on two public implicit feedback data demonstrate the effectiveness of our proposed ONCF framework, in particular, the positive effect of using outer product to model the correlations between embedding dimensions in the low level of multi-layer neural recommender model. The experiment codes are available at: this https URL

Connected through hyperedge HE-1 (induced by source paper "sequential recommendation with dual side neighbor based collaborative relation modeling"):
  with members:
    - "lifelong sequential modeling with personalized memorization for user response prediction"
    - "dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems"
    - "session based social recommendation via dynamic graph attention networks"
    - "deep learning based recommender system a survey and new perspectives"
    - "repeatnet a repeat aware neural recommendation machine for session based recommendation"
    - "bidding machine learning to bid for directly optimizing profits in display advertising"
    - "recurrent neural networks for long and short term sequential recommendation"
    - "recurrent neural networks with top k gains for session based recommendations"

Connected through hyperedge HE-2 (induced by source paper "correlation net spatiotemporal multimodal deep learning for action recognition"):
  with members:
    - "neural collaborative filtering"
    - "ts lstm and temporal inception exploiting spatiotemporal dynamics for activity recognition"
    - "scene flow to action map a new representation for rgb d based action recognition with convolutional neural networks"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "long term temporal convolutions for action recognition"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-3 (induced by source paper "leveraging cross feedback of user and item embeddings for variational autoencoder based collaborative filtering"):
  with members:
    - "neural collaborative filtering"

Connected through hyperedge HE-4 (induced by source paper "towards amortized ranking critical training for collaborative filtering"):
  with members:
    - "eigenrec generalizing puresvd for effective and efficient top n recommendations"
    - "embarrassingly shallow autoencoders for sparse data"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "deep learning based recommender system a survey and new perspectives"
    - "cgans with projection discriminator"
    - "adversarial personalized ranking for recommendation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "recurrent neural networks with top k gains for session based recommendations"

Connected through hyperedge HE-5 (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"
    - "adversarial personalized ranking for recommendation"
    - "a simple convolutional generative network for next item recommendation"
    - "attentive long short term preference modeling for personalized product search"

Connected through hyperedge HE-6 (induced by source paper "deep collaborative filtering with multi aspect information in heterogeneous networks"):
  with members:
    - "prsim sublinear time simrank computation on large power law graphs"
    - "deep learning based recommender system a survey and new perspectives"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural brane neural bayesian personalized ranking for attributed network embedding"
    - "neural factorization machines for sparse predictive analytics"
    - "attention is all you need"
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"

Connected through hyperedge HE-7 (induced by source paper "collaborative filtering with a synthetic feedback loop"):
  with members:
    - "topic guided variational autoencoders for text generation"
    - "improving textual network learning with variational homophilic embeddings"
    - "neurec on nonlinear transformation for personalized ranking"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "wasserstein autoencoders for collaborative filtering"
    - "top k off policy correction for a reinforce recommender system"
    - "zero shot learning via class conditioned deep generative models"
    - "neural collaborative filtering"

Connected through hyperedge HE-8 (induced by source paper "jointly learning explainable rules for recommendation with knowledge graph"):
  with members:
    - "earl joint entity and relation linking for question answering over knowledge graphs"
    - "deep reinforcement learning for page wise recommendations"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "explainable recommendation a survey and new perspectives"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"

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)