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: "multi task feature learning for knowledge graph enhanced recommendation"
  Abstract: Collaborative filtering often suffers from sparsity and cold start problems in real recommendation scenarios, therefore, researchers and engineers usually use side information to address the issues and improve the performance of recommender systems. In this paper, we consider knowledge graphs as the source of side information. We propose MKR, a Multi-task feature learning approach for Knowledge graph enhanced Recommendation. MKR is a deep end-to-end framework that utilizes knowledge graph embedding task to assist recommendation task. The two tasks are associated by cross&compress units, which automatically share latent features and learn high-order interactions between items in recommender systems and entities in the knowledge graph. We prove that cross&compress units have sufficient capability of polynomial approximation, and show that MKR is a generalized framework over several representative methods of recommender systems and multi-task learning. Through extensive experiments on real-world datasets, we demonstrate that MKR achieves substantial gains in movie, book, music, and news recommendation, over state-of-the-art baselines. MKR is also shown to be able to maintain a decent performance even if user-item interactions are sparse.

Connected through hyperedge HE-1 (induced by source paper "entity context and relational paths for knowledge graph completion"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "drum end to end differentiable rule mining on knowledge graphs"
    - "coke contextualized knowledge graph embedding"
    - "iteratively learning embeddings and rules for knowledge graph reasoning"
    - "position aware graph neural networks"
    - "gnnexplainer generating explanations for graph neural networks"
    - "knowledge graph convolutional networks for recommender systems"

Connected through hyperedge HE-2 (induced by source paper "neuro symbolic architectures for context understanding"):
  with members:
    - "an evaluation of knowledge graph embeddings for autonomous driving data experience and practice"
    - "option comparison network for multiple choice reading comprehension"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "knowledge graph embedding for ecotoxicological effect prediction"
    - "kagnet knowledge aware graph networks for commonsense reasoning"
    - "driving datasets literature review"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "dream a challenge dataset and models for dialogue based reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "an evaluation of knowledge graph embeddings for autonomous driving data experience and practice"):
  with members:
    - "knowledge graph embedding for ecotoxicological effect prediction"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "multilingual knowledge graph embeddings for cross lingual knowledge alignment"
    - "from one point to a manifold knowledge graph embedding for precise link prediction"
    - "holographic embeddings of knowledge graphs"

Connected through hyperedge HE-4 (induced by source paper "relation adversarial network for low resource knowledgegraph completion"):
  with members:
    - "long tail relation extraction via knowledge graph embeddings and graph convolution networks"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "kg bert bert for knowledge graph completion"
    - "ernie enhanced language representation with informative entities"
    - "iteratively learning embeddings and rules for knowledge graph reasoning"
    - "transfer learning for relation extraction via relation gated adversarial learning"
    - "meta relational learning for few shot link prediction in knowledge graphs"
    - "adversarial domain adaptation for duplicate question detection"

Connected through hyperedge HE-5 (induced by source paper "attentive knowledge graph embedding for personalized recommendation"):
  with members:
    - "session based recommendation with graph neural network"
    - "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-6 (induced by source paper "explainable knowledge graph based recommendation via deep reinforcement learning"):
  with members:
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
    - "session based social recommendation via dynamic graph attention networks"
    - "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-7 (induced by source paper "fast and accurate knowledge aware document representation enhancement for news recommendations"):
  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"
    - "knowledge graph convolutional networks for recommender systems"
    - "npa neural news recommendation with personalized attention"
    - "graph convolutional neural networks for web scale recommender systems"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"

Connected through hyperedge HE-8 (induced by source paper "a causal perspective to unbiased conversion rate estimation on data missing not at random"):
  with members:
    - "unbiased learning to rank with unbiased propensity estimation"
    - "the deconfounded recommender a causal inference approach to recommendation"
    - "more robust doubly robust off policy evaluation"
    - "rank and rate multi task learning for recommender systems"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "recommendations as treatments debiasing learning and evaluation"

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)