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: "neural collective entity linking"
  Abstract: Entity Linking aims to link entity mentions in texts to knowledge bases, and neural models have achieved recent success in this task. However, most existing methods rely on local contexts to resolve entities independently, which may usually fail due to the data sparsity of local information. To address this issue, we propose a novel neural model for collective entity linking, named as NCEL. NCEL applies Graph Convolutional Network to integrate both local contextual features and global coherence information for entity linking. To improve the computation efficiency, we approximately perform graph convolution on a subgraph of adjacent entity mentions instead of those in the entire text. We further introduce an attention scheme to improve the robustness of NCEL to data noise and train the model on Wikipedia hyperlinks to avoid overfitting and domain bias. In experiments, we evaluate NCEL on five publicly available datasets to verify the linking performance as well as generalization ability. We also conduct an extensive analysis of time complexity, the impact of key modules, and qualitative results, which demonstrate the effectiveness and efficiency of our proposed method.

Connected through hyperedge HE-1 (induced by source paper "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"):
  with members:
    - "temporal relational ranking for stock prediction"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "learning over knowledge base embeddings for recommendation"
    - "joint representation learning of cross lingual words and entities via attentive distant supervision"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "adversarial personalized ranking for recommendation"
    - "explicit state tracking with semi supervisionfor neural dialogue generation"
    - "visually explainable recommendation"

Connected through hyperedge HE-2 (induced by source paper "kgat knowledge graph attention network for recommendation"):
  with members:
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
    - "neural graph collaborative filtering"
    - "learning heterogeneous knowledge base embeddings for explainable recommendation"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "joint representation learning of cross lingual words and entities via attentive distant supervision"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "representation learning on graphs with jumping knowledge networks"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"

Connected through hyperedge HE-3 (induced by source paper "neural attentive bag of entities model for text classification"):
  with members:
    - "end to end neural entity linking"
    - "studio ousia s quiz bowl question answering system"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "wikipedia2vec an optimized tool for learning embeddings of words and entities from wikipedia"
    - "representation learning of entities and documents from knowledge base descriptions"
    - "learning distributed representations of texts and entities from knowledge base"
    - "named entity disambiguation for noisy text"
    - "deep joint entity disambiguation with local neural attention"

Connected through hyperedge HE-4 (induced by source paper "low resource name tagging learned with weakly labeled data"):
  with members:
    - "few shot classification in named entity recognition task"
    - "improving neural relation extraction with implicit mutual relations"
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
    - "multi channel graph neural network for entity alignment"
    - "joint representation learning of cross lingual words and entities via attentive distant supervision"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "few shot learning for named entity recognition in medical text"
    - "meta transfer learning for few shot learning"

Connected through hyperedge HE-5 (induced by source paper "joint learning of named entity recognition and entity linking"):
  with members:
    - "end to end neural entity linking"
    - "improving entity linking by modeling latent relations between mentions"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "collective entity disambiguation with structured gradient tree boosting"
    - "learning distributed representations of texts and entities from knowledge base"
    - "deep joint entity disambiguation with local neural attention"

Connected through hyperedge HE-6 (induced by source paper "zero shot entity linking with dense entity retrieval"):
  with members:
    - "learning dense representations for entity retrieval"
    - "fine grained entity typing for domain independent entity linking"
    - "zero shot entity linking by reading entity descriptions"
    - "poly encoders transformer architectures and pre training strategies for fast and accurate multi sentence scoring"
    - "pytorch biggraph a large scale graph embedding system"
    - "scalable graph based individual named entity identification"
    - "end to end neural entity linking"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "pre training of deep contextualized embeddings of words and entities for named entity disambiguation"):
  with members:
    - "improving entity linking by modeling latent relations between mentions"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "collective entity disambiguation with structured gradient tree boosting"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "attention is all you need"
    - "learning distributed representations of texts and entities from knowledge base"
    - "named entity disambiguation for noisy text"
    - "deep joint entity disambiguation with local neural attention"

Connected through hyperedge HE-8 (induced by source paper "neural collective entity linking based on recurrent random walk network learning"):
  with members:
    - "graph based neural sentence ordering"
    - "semantic neural machine translation using amr"
    - "end to end neural entity linking"
    - "improving entity linking by modeling latent relations between mentions"
    - "sentence state lstm for text representation"
    - "attention is all you need"
    - "deep joint entity disambiguation with local neural attention"
    - "capturing semantic similarity for entity linking with convolutional neural networks"

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.CL(Computation and Language)