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: "ripplenet propagating user preferences on the knowledge graph for recommender systems"
  Abstract: To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance. This paper considers the knowledge graph as the source of side information. To address the limitations of existing embedding-based and path-based methods for knowledge-graph-aware recommendation, we propose RippleNet, an end-to-end framework that naturally incorporates the knowledge graph into recommender systems. Similar to actual ripples propagating on the water, RippleNet stimulates the propagation of user preferences over the set of knowledge entities by automatically and iteratively extending a user's potential interests along links in the knowledge graph. The multiple "ripples" activated by a user's historically clicked items are thus superposed to form the preference distribution of the user with respect to a candidate item, which could be used for predicting the final clicking probability. Through extensive experiments on real-world datasets, we demonstrate that RippleNet achieves substantial gains in a variety of scenarios, including movie, book and news recommendation, over several state-of-the-art baselines.

Connected through hyperedge HE-1 (induced by source paper "a survey on knowledge graph based recommender systems"):
  with members:
    - "explainable knowledge graph based recommendation via deep reinforcement learning"
    - "deep short text classification with knowledge powered attention"
    - "research commentary on recommendations with side information a survey and research directions"
    - "relational collaborative filtering modeling multiple item relations for recommendation"
    - "kgat knowledge graph attention network for recommendation"
    - "intentgc a scalable graph convolution framework fusing heterogeneous information for recommendation"
    - "an end to end neighborhood based interaction model for knowledge enhanced recommendation"
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"

Connected through hyperedge HE-2 (induced by source paper "reinforced negative sampling over knowledge graph for recommendation"):
  with members:
    - "kgat knowledge graph attention network for recommendation"
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
    - "reinforcement knowledge graph reasoning for explainable recommendation"
    - "knowledge aware graph neural networks with label smoothness regularization for recommender systems"
    - "neural graph collaborative filtering"
    - "adversarial sampling and training for semi supervised information retrieval"
    - "learning heterogeneous knowledge base embeddings for explainable recommendation"
    - "explainable reasoning over knowledge graphs for recommendation"

Connected through hyperedge HE-3 (induced by source paper "product knowledge graph embedding for e commerce"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "a multi task learning approach for improving product title compression with user search log data"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "multi task learning as multi objective optimization"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "attention is all you need"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "poincar e embeddings for learning hierarchical representations"

Connected through hyperedge HE-4 (induced by source paper "prince provider side interpretability with counterfactual explanations in recommender systems"):
  with members:
    - "jointly learning explainable rules for recommendation with knowledge graph"
    - "fairy a framework for understanding relationships between users actions and their social feeds"
    - "reinforcement knowledge graph reasoning for explainable recommendation"
    - "learning heterogeneous knowledge base embeddings for explainable recommendation"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "explainable recommendation via multi task learning in opinionated text data"
    - "explainable recommendation a survey and new perspectives"
    - "aurora auditing pagerank on large graphs"

Connected through hyperedge HE-5 (induced by source paper "syndrome aware herb recommendation with multi graph convolution network"):
  with members:
    - "beyond clicks modeling multi relational item graph for session based target behavior prediction"
    - "session based recommendation with graph neural network"
    - "kgat knowledge graph attention network for recommendation"
    - "graph neural networks for social recommendation"
    - "neural graph collaborative filtering"
    - "graph convolutional neural networks for web scale recommender systems"
    - "socialgcn an efficient graph convolutional network based model for social recommendation"
    - "exploration on generating traditional chinese medicine prescription from symptoms with an end to end method"

Connected through hyperedge HE-6 (induced by source paper "towards knowledge based recommender dialog system"):
  with members:
    - "towards knowledge based personalized product description generation in e commerce"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "deep conversational recommender in travel"
    - "graph convolutional neural networks for web scale recommender systems"
    - "towards deep conversational recommendations"
    - "generating wikipedia by summarizing long sequences"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "explainable recommendation a survey and new perspectives"

Connected through hyperedge HE-7 (induced by source paper "attentive knowledge graph embedding for personalized recommendation"):
  with members:
    - "session based recommendation with graph neural network"
    - "multi task feature learning for knowledge graph enhanced recommendation"
    - "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"
    - "knowledge aware graph neural networks with label smoothness regularization for recommender systems"
    - "graph neural networks for social recommendation"
    - "knowledge graph convolutional networks for recommender systems"

Connected through hyperedge HE-8 (induced by source paper "relational collaborative filtering modeling multiple item relations for recommendation"):
  with members:
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
    - "neural graph collaborative filtering"
    - "recommendation through mixtures of heterogeneous item relationships"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "learning heterogeneous knowledge base embeddings for explainable recommendation"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "adversarial personalized ranking for recommendation"
    - "deep item based collaborative filtering for top n 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)