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 recommendation with graph neural network"
  Abstract: The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representations besides item representations to make recommendations. Though achieved promising results, they are insufficient to obtain accurate user vectors in sessions and neglect complex transitions of items. To obtain accurate item embedding and take complex transitions of items into account, we propose a novel method, i.e. Session-based Recommendation with Graph Neural Networks, SR-GNN for brevity. In the proposed method, session sequences are modeled as graphstructured data. Based on the session graph, GNN can capture complex transitions of items, which are difficult to be revealed by previous conventional sequential methods. Each session is then represented as the composition of the global preference and the current interest of that session using an attention network. Extensive experiments conducted on two real datasets show that SR-GNN evidently outperforms the state-of-the-art session-based recommendation methods consistently.

Connected through hyperedge HE-1 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis"):
  with members:
    - "contextual hybrid session based news recommendation with recurrent neural networks"
    - "on the importance of news content representation in hybrid neural session based recommender systems"
    - "deep learning based recommender system a survey and new perspectives"
    - "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 "graph universal adversarial attacks a few bad actors ruin graph learning models"):
  with members:
    - "power up robust graph convolutional network against evasion attacks based on graph powering"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "adversarial attacks on graph neural networks via meta learning"
    - "universal transferable and targeted adversarial attacks"
    - "adversarial attack on graph structured data"
    - "adversarial patch"
    - "attack graph convolutional networks by adding fake nodes"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-3 (induced by source paper "beyond clicks modeling multi relational item graph for session based target behavior prediction"):
  with members:
    - "bert4rec sequential recommendation with bidirectional encoder representations from transformer"
    - "hierarchical temporal convolutional networks for dynamic recommender systems"
    - "repeatnet a repeat aware neural recommendation machine for session based recommendation"
    - "attention is all you need"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "improved recurrent neural networks for session based recommendations"
    - "dual attention networks for multimodal reasoning and matching"

Connected through hyperedge HE-4 (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"
    - "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"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"

Connected through hyperedge HE-5 (induced by source paper "deep learning for sequential recommendation algorithms influential factors and evaluations"):
  with members:
    - "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"
    - "deep learning based recommender system a survey and new perspectives"
    - "graph neural networks a review of methods and applications"

Connected through hyperedge HE-6 (induced by source paper "accelerated learning from recommender systems using multi armed bandit"):
  with members:
    - "productization challenges of contextual multi armed bandits"
    - "perturbed history exploration in stochastic multi armed bandits"
    - "on the difficulty of evaluating baselines a study on recommender systems"
    - "a batched multi armed bandit approach to news headline testing"
    - "a simple deep personalized recommendation system"
    - "a survey on session based recommender systems"
    - "multiplayer bandits without observing collision information"
    - "ucboost a boosting approach to tame complexity and optimality for stochastic bandits"

Connected through hyperedge HE-7 (induced by source paper "attentive knowledge graph embedding for personalized recommendation"):
  with members:
    - "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"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"

Connected through hyperedge HE-8 (induced by source paper "dressing as a whole outfit compatibility learning based on node wise graph neural networks"):
  with members:
    - "constructing narrative event evolutionary graph for script event prediction"
    - "situation recognition with graph neural networks"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "compatibility family learning for item recommendation and generation"
    - "style2vec representation learning for fashion items from style sets"
    - "learning fashion compatibility with bidirectional lstms"
    - "mining fashion outfit composition using an end to end deep learning approach on set data"

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)