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: "reinforcement knowledge graph reasoning for explainable recommendation"
  Abstract: Recent advances in personalized recommendation have sparked great interest in the exploitation of rich structured information provided by knowledge graphs. Unlike most existing approaches that only focus on leveraging knowledge graphs for more accurate recommendation, we aim to conduct explicit reasoning with knowledge for decision making so that the recommendations are generated and supported by an interpretable causal inference procedure. To this end, we propose a method called Policy-Guided Path Reasoning (PGPR), which couples recommendation and interpretability by providing actual paths in a knowledge graph. Our contributions include four aspects. We first highlight the significance of incorporating knowledge graphs into recommendation to formally define and interpret the reasoning process. Second, we propose a reinforcement learning (RL) approach featured by an innovative soft reward strategy, user-conditional action pruning and a multi-hop scoring function. Third, we design a policy-guided graph search algorithm to efficiently and effectively sample reasoning paths for recommendation. Finally, we extensively evaluate our method on several large-scale real-world benchmark datasets, obtaining favorable results compared with state-of-the-art methods.

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"
    - "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"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "explainable reasoning over knowledge graphs for recommendation"

Connected through hyperedge HE-3 (induced by source paper "heterogeneous relational reasoning in knowledge graphs with reinforcement learning"):
  with members:
    - "graph few shot learning via knowledge transfer"
    - "reinforcewalk learning to walk in graph with monte carlo tree search"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "top k off policy correction for a reinforce recommender system"
    - "deep reinforcement learning based recommendation with explicit user item interactions modeling"
    - "one shot relational learning for knowledge graphs"
    - "m walk learning to walk over graphs using monte carlo tree search"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "absent cross lingual sentence representation mapping with bidirectional gans"):
  with members:
    - "improving multilingual sentence embedding using bi directional dual encoder with additive margin softmax"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "oogan disentangling gan with one hot sampling and orthogonal regularization"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "characterizing departures from linearity in word translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning joint multilingual sentence representations with neural machine translation"
    - "triangle generative adversarial networks"

Connected through hyperedge HE-5 (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"
    - "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"
    - "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-6 (induced by source paper "asymmetrical hierarchical networks with attentive interactions for interpretable review based recommendation"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "attention based deep multiple instance learning"
    - "deep contextualized word representations"
    - "multi pointer co attention networks for recommendation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a co matching model for multi choice reading comprehension"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "transnets learning to transform for 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)