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: "context dependent fine grained entity type tagging"
  Abstract: Entity type tagging is the task of assigning category labels to each mention of an entity in a document. While standard systems focus on a small set of types, recent work (Ling and Weld, 2012) suggests that using a large fine-grained label set can lead to dramatic improvements in downstream tasks. In the absence of labeled training data, existing fine-grained tagging systems obtain examples automatically, using resolved entities and their types extracted from a knowledge base. However, since the appropriate type often depends on context (e.g. Washington could be tagged either as city or government), this procedure can result in spurious labels, leading to poorer generalization. We propose the task of context-dependent fine type tagging, where the set of acceptable labels for a mention is restricted to only those deducible from the local context (e.g. sentence or document). We introduce new resources for this task: 11,304 mentions annotated with their context-dependent fine types, and we provide baseline experimental results on this data.

Connected through hyperedge HE-1 (induced by source paper "improving entity linking by modeling latent entity type information"):
  with members:
    - "joint learning of named entity recognition and entity linking"
    - "a comprehensive exploration on wikisql with table aware word contextualization"
    - "zero shot open entity typing as type compatible grounding"
    - "boosting entity linking performance by leveraging unlabeled documents"
    - "learning dynamic context augmentation for global entity linking"
    - "distant learning for entity linking with automatic noise detection"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "improving entity linking by modeling latent relations between mentions"

Connected through hyperedge HE-2 (induced by source paper "a neural architecture for person ontology population"):
  with members:
    - "position aware graph neural networks"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "neural fine grained entity type classification with hierarchy aware loss"
    - "ultra fine entity typing"
    - "fine grained classification of personal data entities"
    - "universal sentence encoder"
    - "on2vec embedding based relation prediction for ontology population"
    - "finer grained entity typing with typenet"

Connected through hyperedge HE-3 (induced by source paper "data augmentation for personal knowledge graph population"):
  with members:
    - "a neural architecture for person ontology population"
    - "position aware graph neural networks"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "fine grained classification of personal data entities"
    - "counterfactual fairness in text classification through robustness"
    - "collective learning from diverse datasets for entity typing in the wild"
    - "simple and deterministic matrix sketching"

Connected through hyperedge HE-4 (induced by source paper "multi task learning with contextualized word representations for extented named entity recognition"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "multi task learning for sequence tagging an empirical study"
    - "neural adaptation layers for cross domain named entity recognition"
    - "end to end recurrent neural network models for vietnamese named entity recognition word level vs character level"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "semi supervised multitask learning for sequence labeling"

Connected through hyperedge HE-5 (induced by source paper "learning to learn and predict a meta learning approach for multi label classification"):
  with members:
    - "reinforced co training"
    - "deep reinforcement learning that matters"
    - "doctag2vec an embedding based multi label learning approach for document tagging"
    - "prototypical networks for few shot learning"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "fine grained entity type classification by jointly learning representations and label embeddings"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to learn by gradient descent by gradient descent"

Connected through hyperedge HE-6 (induced by source paper "fine grained entity typing for domain independent entity linking"):
  with members:
    - "learning to denoise distantly labeled data for entity typing"
    - "boosting entity linking performance by leveraging unlabeled documents"
    - "ultra fine entity typing"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deeptype multilingual entity linking by neural type system evolution"
    - "hierarchical losses and new resources for fine grained entity typing and linking"
    - "end to end neural coreference resolution"

Connected through hyperedge HE-7 (induced by source paper "improving distantly supervised entity typing with compact latent space clustering"):
  with members:
    - "a unified labeling approach by pooling diverse datasets for entity typing"
    - "semi supervised learning via compact latent space clustering"
    - "deeptype multilingual entity linking by neural type system evolution"
    - "collective learning from diverse datasets for entity typing in the wild"
    - "attention is all you need"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "fine grained entity type classification by jointly learning representations and label embeddings"
    - "corpus level fine grained entity typing using contextual information"

Connected through hyperedge HE-8 (induced by source paper "imposing label relational inductive bias for extremely fine grained entity typing"):
  with members:
    - "neural fine grained entity type classification with hierarchy aware loss"
    - "ultra fine entity typing"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "fine grained entity typing with high multiplicity assignments"
    - "learned in translation contextualized word vectors"
    - "label noise reduction in entity typing by heterogeneous partial label embedding"
    - "enhanced lstm for natural language inference"
    - "neural architectures for fine grained entity type classification"

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)