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: "multi pointer co attention networks for recommendation"
  Abstract: Many recent state-of-the-art recommender systems such as D-ATT, TransNet and DeepCoNN exploit reviews for representation learning. This paper proposes a new neural architecture for recommendation with reviews. Our model operates on a multi-hierarchical paradigm and is based on the intuition that not all reviews are created equal, i.e., only a selected few are important. The importance, however, should be dynamically inferred depending on the current target. To this end, we propose a review-by-review pointer-based learning scheme that extracts important reviews from user and item reviews and subsequently matches them in a word-by-word fashion. This enables not only the most informative reviews to be utilized for prediction but also a deeper word-level interaction. Our pointer-based method operates with a gumbel-softmax based pointer mechanism that enables the incorporation of discrete vectors within differentiable neural architectures. Our pointer mechanism is co-attentive in nature, learning pointers which are co-dependent on user-item relationships. Finally, we propose a multi-pointer learning scheme that learns to combine multiple views of user-item interactions. We demonstrate the effectiveness of our proposed model via extensive experiments on 24 benchmark datasets from Amazon and Yelp. Empirical results show that our approach significantly outperforms existing state-of-the-art models, with up to 19% and 71% relative improvement when compared to TransNet and DeepCoNN respectively. We study the behavior of our multi-pointer learning mechanism, shedding light on 'evidence aggregation' patterns in review-based recommender systems.

Connected through hyperedge HE-1 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis"):
  with members:
    - "session based recommendation with graph neural network"
    - "contextual hybrid session based news recommendation with recurrent neural networks"
    - "on the importance of news content representation in hybrid neural session based recommender systems"
    - "deep learning based recommender system a survey and new perspectives"
    - "neurec on nonlinear transformation for personalized ranking"
    - "sequence aware recommender systems"
    - "evaluation of session based recommendation algorithms"
    - "neural collaborative ranking"

Connected through hyperedge HE-2 (induced by source paper "cata a collaborative dual attentive autoencoder method for recommending scientific articles"):
  with members:
    - "nais neural attentive item similarity model for recommendation"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "collaborative deep learning for recommender systems"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "two headed monster and crossed co attention networks"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "pay less attention with lightweight and dynamic convolutions"
    - "the evolved transformer"
    - "context aware self attention networks"
    - "modeling recurrence for transformer"
    - "adaptive attention span in transformers"
    - "scaling neural machine translation"
    - "modeling localness for self attention networks"

Connected through hyperedge HE-4 (induced by source paper "signed distance based deep memory recommender"):
  with members:
    - "fine grained attention mechanism for neural machine translation"
    - "regularizing matrix factorization with user and item embeddings for recommendation"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "collaborative memory network for recommendation systems"
    - "gated attentive autoencoder for content aware recommendation"
    - "point of interest recommendation exploiting self attentive autoencoders with neighbor aware influence"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "diversified co attention towards informative live video commenting"):
  with members:
    - "automatic generation of personalized comment based on user profile"
    - "deep modular co attention networks for visual question answering"
    - "livebot generating live video comments based on visual and textual contexts"
    - "move forward and tell a progressive generator of video descriptions"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "automatic article commenting the task and dataset"
    - "joint event detection and description in continuous video streams"
    - "learning comment generation by leveraging user generated data"

Connected through hyperedge HE-6 (induced by source paper "multi scale quasi rnn for next item recommendation"):
  with members:
    - "session based recommendation with graph neural network"
    - "deep learning based recommender system a survey and new perspectives"
    - "neurec on nonlinear transformation for personalized ranking"
    - "sequence aware recommender systems"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "a simple convolutional generative network for next item recommendation"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "self attentive sequential recommendation"

Connected through hyperedge HE-7 (induced by source paper "collaborative self attention for recommender systems"):
  with members:
    - "convolutional geometric matrix completion"
    - "outer product based neural collaborative filtering"
    - "attention is all you need"
    - "neural collaborative filtering"
    - "deep semantic role labeling with self attention"
    - "global optimality in low rank matrix optimization"
    - "scalable robust matrix factorization with nonconvex loss"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"

Connected through hyperedge HE-8 (induced by source paper "a hierarchical self attentive model for recommending user generated item lists"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "pseudo implicit feedback for alleviating data sparsity in top k recommendation"
    - "point of interest recommendation exploiting self attentive autoencoders with neighbor aware influence"
    - "next item recommendation with self attention"
    - "self attentive sequential recommendation"
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "attention is all you need"
    - "neural 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.CL(Computation and Language)