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: "hierarchical losses and new resources for fine grained entity typing and linking"
  Abstract: Extraction from raw text to a knowledge base of entities and fine-grained types is often cast as prediction into a flat set of entity and type labels, neglecting the rich hierarchies over types and entities contained in curated ontologies. Previous attempts to incorporate hierarchical structure have yielded little benefit and are restricted to shallow ontologies. This paper presents new methods using real and complex bilinear mappings for integrating hierarchical information, yielding substantial improvement over flat predictions in entity linking and fine-grained entity typing, and achieving new state-of-the-art results for end-to-end models on the benchmark FIGER dataset. We also present two new human-annotated datasets containing wide and deep hierarchies which we will release to the community to encourage further research in this direction: MedMentions, a collection of PubMed abstracts in which 246k mentions have been mapped to the massive UMLS ontology; and TypeNet, which aligns Freebase types with the WordNet hierarchy to obtain nearly 2k entity types. In experiments on all three datasets we show substantial gains from hierarchy-aware training.

Connected through hyperedge HE-1 (induced by source paper "learning cross context entity representations from text"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "end to end neural entity linking"
    - "ultra fine entity typing"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "additive margin softmax for face verification"
    - "deeptype multilingual entity linking by neural type system evolution"

Connected through hyperedge HE-2 (induced by source paper "fine grained entity typing in hyperbolic space"):
  with members:
    - "imposing label relational inductive bias for extremely fine grained entity typing"
    - "hyperbolic attention networks"
    - "skip gram word embeddings in hyperbolic space"
    - "hyperbolic representation learning for fast and efficient neural question answering"
    - "ultra fine entity typing"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "embedding text in hyperbolic spaces"
    - "representation tradeoffs for hyperbolic embeddings"

Connected through hyperedge HE-3 (induced by source paper "latte latent type modeling for biomedical entity linking"):
  with members:
    - "medmentions a large biomedical corpus annotated with umls concepts"
    - "improving entity linking by modeling latent relations between mentions"
    - "deeptype multilingual entity linking by neural type system evolution"
    - "improving hospital mortality prediction with medical named entities and multimodal learning"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "capturing semantic similarity for entity linking with convolutional neural networks"
    - "a deep relevance matching model for ad hoc retrieval"
    - "bidirectional attention flow for machine comprehension"

Connected through hyperedge HE-4 (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"
    - "end to end neural coreference resolution"
    - "named entity disambiguation for noisy text"

Connected through hyperedge HE-5 (induced by source paper "zero shot entity linking by reading entity descriptions"):
  with members:
    - "cross domain regularization for neural ranking models using adversarial learning"
    - "multi source domain adaptation with mixture of experts"
    - "improving entity linking by modeling latent relations between mentions"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "knowledge based word sense disambiguation using topic models"
    - "universal language model fine tuning for text classification"

Connected through hyperedge HE-6 (induced by source paper "learning to denoise distantly labeled data for entity typing"):
  with members:
    - "imposing label relational inductive bias for extremely fine grained entity typing"
    - "ultra fine entity typing"
    - "deep contextualized word representations"
    - "deep probabilistic logic a unifying framework for indirect supervision"
    - "end to end neural coreference resolution"
    - "learning with noise enhance distantly supervised relation extraction with dynamic transition matrix"
    - "snorkel rapid training data creation with weak supervision"
    - "multi level representations for fine grained typing of knowledge base entities"

Connected through hyperedge HE-7 (induced by source paper "leveraging external knowledge for out of vocabulary entity labeling"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "multi context label embedding"
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "contextual slot carryover for disparate schemas"
    - "prototypical networks for few shot learning"
    - "poincar e embeddings for learning hierarchical representations"
    - "adam a method for stochastic optimization"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"

Connected through hyperedge HE-8 (induced by source paper "scispacy fast and robust models for biomedical natural language processing"):
  with members:
    - "structural scaffolds for citation intent classification in scientific publications"
    - "from pos tagging to dependency parsing for biomedical event extraction"
    - "extending a parser to distant domains using a few dozen partially annotated examples"
    - "cross type biomedical named entity recognition with deep multi task learning"
    - "construction of the literature graph in semantic scholar"
    - "effective use of bidirectional language modeling for transfer learning in biomedical named entity recognition"
    - "deep biaffine attention for neural dependency parsing"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"

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)