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: "session based social recommendation via dynamic graph attention networks"
  Abstract: Online communities such as Facebook and Twitter are enormously popular and have become an essential part of the daily life of many of their users. Through these platforms, users can discover and create information that others will then consume. In that context, recommending relevant information to users becomes critical for viability. However, recommendation in online communities is a challenging problem: 1) users' interests are dynamic, and 2) users are influenced by their friends. Moreover, the influencers may be context-dependent. That is, different friends may be relied upon for different topics. Modeling both signals is therefore essential for recommendations. We propose a recommender system for online communities based on a dynamic-graph-attention neural network. We model dynamic user behaviors with a recurrent neural network, and context-dependent social influence with a graph-attention neural network, which dynamically infers the influencers based on users' current interests. The whole model can be efficiently fit on large-scale data. Experimental results on several real-world data sets demonstrate the effectiveness of our proposed approach over several competitive baselines including state-of-the-art models.

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 "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"
    - "deep learning based recommender system a survey and new perspectives"
    - "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-3 (induced by source paper "discovering strategic behaviors for collaborative content production in social networks"):
  with members:
    - "inf vae a variational autoencoder framework to integrate homophily and influence in diffusion prediction"
    - "an induced multi relational framework for answer selection in community question answer platforms"
    - "dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "attention models in graphs a survey"
    - "deep learning on graphs a survey"
    - "dynamic graph representation learning via self attention networks"

Connected through hyperedge HE-4 (induced by source paper "autoint automatic feature interaction learning via self attentive neural networks"):
  with members:
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "nais neural attentive item similarity model for recommendation"
    - "neural factorization machines for sparse predictive analytics"
    - "attention is all you need"
    - "deep embedding forest forest based serving with deep embedding features"
    - "deep cross network for ad click predictions"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-5 (induced by source paper "explainable knowledge graph based recommendation via deep reinforcement learning"):
  with members:
    - "multi task feature learning for knowledge graph enhanced recommendation"
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "learning over knowledge base embeddings for 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"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"

Connected through hyperedge HE-6 (induced by source paper "hyperbolic graph attention network"):
  with members:
    - "constant curvature graph convolutional networks"
    - "hyperbolic graph neural networks"
    - "hyperbolic graph convolutional neural networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hyperbolic neural networks"
    - "representation tradeoffs for hyperbolic embeddings"
    - "hyperbolic entailment cones for learning hierarchical embeddings"
    - "geometric deep learning going beyond euclidean data"

Connected through hyperedge HE-7 (induced by source paper "beyond personalization social content recommendation for creator equality and consumer satisfaction"):
  with members:
    - "large scale interactive recommendation with tree structured policy gradient"
    - "neural collaborative filtering"
    - "collaborative filtering with social local models"
    - "collaborative deep learning for recommender systems"
    - "convolutional neural networks for sentence classification"
    - "neural machine translation by jointly learning to align and translate"
    - "exploration in interactive personalized music recommendation a reinforcement learning approach"
    - "a contextual bandit approach to personalized news article 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)