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 item based collaborative filtering for top n recommendation"
  Abstract: Item-based Collaborative Filtering(short for ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user's profile with the items that the user has consumed, ICF recommends items that are similar to the user's profile. With the prevalence of machine learning in recent years, significant processes have been made for ICF by learning item similarity (or representation) from data. Nevertheless, we argue that most existing works have only considered linear and shallow relationship between items, which are insufficient to capture the complicated decision-making process of users. #R##N#In this work, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationship among items. Going beyond modeling only the second-order interaction (e.g. similarity) between two items, we additionally consider the interaction among all interacted item pairs by using nonlinear neural networks. Through this way, we can effectively model the higher-order relationship among items, capturing more complicated effects in user decision-making. For example, it can differentiate which historical itemsets in a user's profile are more important in affecting the user to make a purchase decision on an item. We treat this solution as a deep variant of ICF, thus term it as DeepICF. To justify our proposal, we perform empirical studies on two public datasets from MovieLens and Pinterest. Extensive experiments verify the highly positive effect of higher-order item interaction modeling with nonlinear neural networks. Moreover, we demonstrate that by more fine-grained second-order interaction modeling with attention network, the performance of our DeepICF method can be further improved.

Connected through hyperedge HE-1 (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"
    - "deep learning based recommender system a survey and new perspectives"
    - "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"

Connected through hyperedge HE-2 (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-3 (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"
    - "collaborative memory network for recommendation systems"
    - "representation learning on graphs with jumping knowledge networks"

Connected through hyperedge HE-4 (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"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "adversarial personalized ranking for recommendation"

Connected through hyperedge HE-5 (induced by source paper "boosting item based collaborative filtering via nearly uncoupled random walks"):
  with members:
    - "eigenrec generalizing puresvd for effective and efficient top n recommendations"
    - "embarrassingly shallow autoencoders for sparse data"
    - "deep learning based recommender system a survey and new perspectives"
    - "adversarial personalized ranking for recommendation"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "nais neural attentive item similarity model for recommendation"
    - "self attentive sequential recommendation"
    - "neural collaborative filtering"

Connected through hyperedge HE-6 (induced by source paper "model explanations under calibration"):
  with members:
    - "an analysis of attention over clinical notes for predictive tasks"
    - "attention is not explanation"
    - "deep learning based recommender system a survey and new perspectives"
    - "explainable recommendation a survey and new perspectives"
    - "explaining explanations an overview of interpretability of machine learning"
    - "on calibration of modern neural networks"
    - "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.IR(Information Retrieval)