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: "deep learning based recommender system a survey and new perspectives"
  Abstract: With the growing volume of online information, recommender systems have been an effective strategy to overcome information overload. The utility of recommender systems cannot be overstated, given their widespread adoption in many web applications, along with their potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also to the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. The field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning-based recommender systems. More concretely, we provide and devise a taxonomy of deep learning-based recommendation models, along with a comprehensive summary of the state of the art. Finally, we expand on current trends and provide new perspectives pertaining to this new and exciting development of the field.

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"
    - "neurec on nonlinear transformation for personalized ranking"
    - "sequence aware recommender systems"
    - "evaluation of session based recommendation algorithms"
    - "multi pointer co attention networks for recommendation"
    - "neural collaborative ranking"

Connected through hyperedge HE-2 (induced by source paper "autoemb automated embedding dimensionality search in streaming recommendations"):
  with members:
    - "adabert task adaptive bert compression with differentiable neural architecture search"
    - "mixed dimension embeddings with application to memory efficient recommendation systems"
    - "neural input search for large scale recommendation models"
    - "regularized evolution for image classifier architecture search"
    - "neural architecture optimization"
    - "path level network transformation for efficient architecture search"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"

Connected through hyperedge HE-3 (induced by source paper "learning to hash with graph neural networks for recommender systems"):
  with members:
    - "is a single vector enough exploring node polysemy for network embedding"
    - "deep representation learning for social network analysis"
    - "graph neural networks for social recommendation"
    - "neural graph collaborative filtering"
    - "graph convolutional neural networks for web scale recommender systems"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "hierarchical graph representation learning with differentiable pooling"
    - "nash toward end to end neural architecture for generative semantic hashing"

Connected through hyperedge HE-4 (induced by source paper "developing multi task recommendations with long term rewards via policy distilled reinforcement learning"):
  with members:
    - "micro and macro level churn analysis of large scale mobile games"
    - "joint neural collaborative filtering for recommender systems"
    - "value aware recommendation based on reinforced profit maximization in e commerce systems"
    - "ranking distillation learning compact ranking models with high performance for recommender system"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation"
    - "ranking for relevance and display preferences in complex presentation layouts"

Connected through hyperedge HE-5 (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"
    - "outer product based neural collaborative filtering"
    - "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-6 (induced by source paper "context aware recommendations for televisions using deep embeddings with relaxed n pairs loss objective"):
  with members:
    - "deep joint embeddings of context and content for recommendation"
    - "grasp2vec learning object representations from self supervised grasping"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "the importance of context when recommending tv content dataset and algorithms"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "wide deep learning for recommender systems"
    - "meta prod2vec product embeddings using side information for recommendation"

Connected through hyperedge HE-7 (induced by source paper "hacia los comit es de etica en inteligencia artificial"):
  with members:
    - "towards fairer datasets filtering and balancing the distribution of the people subtree in the imagenet hierarchy"
    - "charge based prison term prediction with deep gating network"
    - "read attend and comment a deep architecture for automatic news comment generation"
    - "attention is all you need"
    - "semantics derived automatically from language corpora contain human like biases"
    - "facenet a unified embedding for face recognition and clustering"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-8 (induced by source paper "deep learning for sequential recommendation algorithms influential factors and evaluations"):
  with members:
    - "session based recommendation with graph neural network"
    - "bert4rec sequential recommendation with bidirectional encoder representations from transformer"
    - "learning to recommend with multiple cascading behaviors"
    - "research commentary on recommendations with side information a survey and research directions"
    - "improving end to end sequential recommendations with intent aware diversification"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "a survey on session based recommender systems"
    - "graph neural networks a review of methods and applications"

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)