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: "collaborative memory network for recommendation systems"
  Abstract: Recommendation systems play a vital role to keep users engaged with personalized content in modern online platforms. Deep learning has revolutionized many research fields and there is a recent surge of interest in applying it to collaborative filtering (CF). However, existing methods compose deep learning architectures with the latent factor model ignoring a major class of CF models, neighborhood or memory-based approaches. We propose Collaborative Memory Networks (CMN), a deep architecture to unify the two classes of CF models capitalizing on the strengths of the global structure of latent factor model and local neighborhood-based structure in a nonlinear fashion. Motivated by the success of Memory Networks, we fuse a memory component and neural attention mechanism as the neighborhood component. The associative addressing scheme with the user and item memories in the memory module encodes complex user-item relations coupled with the neural attention mechanism to learn a user-item specific neighborhood. Finally, the output module jointly exploits the neighborhood with the user and item memories to produce the ranking score. Stacking multiple memory modules together yield deeper architectures capturing increasingly complex user-item relations. Furthermore, we show strong connections between CMN components, memory networks and the three classes of CF models. Comprehensive experimental results demonstrate the effectiveness of CMN on three public datasets outperforming competitive baselines. Qualitative visualization of the attention weights provide insight into the model's recommendation process and suggest the presence of higher order interactions.

Connected through hyperedge HE-1 (induced by source paper "lightgcn simplifying and powering graph convolution network for recommendation"):
  with members:
    - "temporal relational ranking for stock prediction"
    - "kgat knowledge graph attention network for recommendation"
    - "a neural influence diffusion model for social recommendation"
    - "collaborative similarity embedding for recommender systems"
    - "λopt learn to regularize recommender models in finer levels"
    - "neural graph collaborative filtering"
    - "knowledge graph convolutional networks for recommender systems"
    - "graph convolutional neural networks for web scale recommender systems"

Connected through hyperedge HE-2 (induced by source paper "attributed multi relational attention network for fact checking url recommendation"):
  with members:
    - "learning from fact checkers analysis and generation of fact checking language"
    - "neural graph collaborative filtering"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "harmonious attention network for person re identification"
    - "large scale learnable graph convolutional networks"
    - "semi supervised content based detection of misinformation via tensor embeddings"
    - "nais neural attentive item similarity model for recommendation"
    - "fake news detection in social networks via crowd signals"

Connected through hyperedge HE-3 (induced by source paper "estimation action reflection towards deep interaction between conversational and recommender systems"):
  with members:
    - "deep conversational recommender in travel"
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "toward building conversational recommender systems a contextual bandit approach"
    - "towards deep conversational recommendations"
    - "learning contextual bandits in a non stationary environment"
    - "explicit state tracking with semi supervisionfor neural dialogue generation"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "conversational recommender system"

Connected through hyperedge HE-4 (induced by source paper "lifelong sequential modeling with personalized memorization for user response prediction"):
  with members:
    - "on training recurrent neural networks for lifelong learning"
    - "learning multi touch conversion attribution with dual attention mechanisms for online advertising"
    - "next item recommendation with self attention"
    - "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"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "self attentive sequential recommendation"

Connected through hyperedge HE-5 (induced by source paper "adversarial mahalanobis distance based attentive song recommender for automatic playlist continuation"):
  with members:
    - "signed distance based deep memory recommender"
    - "a hybrid approach to music playlist continuation based on playlist song membership"
    - "regularizing matrix factorization with user and item embeddings for recommendation"
    - "gated attentive autoencoder for content aware recommendation"
    - "adversarial personalized ranking for recommendation"
    - "point of interest recommendation exploiting self attentive autoencoders with neighbor aware influence"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "automatic playlist continuation through a composition of collaborative filters"

Connected through hyperedge HE-6 (induced by source paper "neural graph collaborative filtering"):
  with members:
    - "relational collaborative filtering modeling multiple item relations for recommendation"
    - "kgat knowledge graph attention network for recommendation"
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
    - "graph convolutional neural networks for web scale recommender systems"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "representation learning on graphs with jumping knowledge networks"
    - "adversarial personalized ranking for recommendation"

Connected through hyperedge HE-7 (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"
    - "gated attentive autoencoder for content aware recommendation"
    - "point of interest recommendation exploiting self attentive autoencoders with neighbor aware influence"
    - "multi pointer co attention networks for recommendation"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "user diverse preference modeling by multimodal attentive metric learning"):
  with members:
    - "person re identification with metric learning using privileged information"
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "metric factorization recommendation beyond matrix factorization"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "neural compatibility modeling with attentive knowledge distillation"
    - "neural collaborative filtering"
    - "joint deep modeling of users and items using reviews for recommendation"

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)