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: "noise mitigation for neural entity typing and relation extraction"
  Abstract: In this paper, we address two different types of noise in information extraction models: noise from distant supervision and noise from pipeline input features. Our target tasks are entity typing and relation extraction. For the first noise type, we introduce multi-instance multi-label learning algorithms using neural network models, and apply them to fine-grained entity typing for the first time. This gives our models comparable performance with the state-of-the-art supervised approach which uses global embeddings of entities. For the second noise type, we propose ways to improve the integration of noisy entity type predictions into relation extraction. Our experiments show that probabilistic predictions are more robust than discrete predictions and that joint training of the two tasks performs best.

Connected through hyperedge HE-1 (induced by source paper "fine tuning pre trained transformer language models to distantly supervised relation extraction"):
  with members:
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "generating wikipedia by summarizing long sequences"
    - "deep contextualized word representations"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "see syntax aware entity embedding for neural relation extraction"
    - "universal language model fine tuning for text classification"

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 "type aware convolutional neural networks for slot filling"):
  with members:
    - "cis at tac cold start 2015 neural networks and coreference resolution for slot filling"
    - "global normalization of convolutional neural networks for joint entity and relation classification"
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "improving slot filling performance with attentive neural networks on dependency structures"
    - "neural architectures for named entity recognition"
    - "comparing convolutional neural networks to traditional models for slot filling"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "combining recurrent and convolutional neural networks for relation classification"

Connected through hyperedge HE-4 (induced by source paper "structured minimally supervised learning for neural relation extraction"):
  with members:
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "combining distant and direct supervision for neural relation extraction"
    - "event representations with tensor based compositions"
    - "weakly supervised relation extraction by pattern enhanced embedding learning"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "globally normalized transition based neural networks"
    - "neural architectures for named entity recognition"
    - "multilingual relation extraction using compositional universal schema"

Connected through hyperedge HE-5 (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"

Connected through hyperedge HE-6 (induced by source paper "neutype a simple and effective neural network approach for predicting missing entity type information in knowledge bases"):
  with members:
    - "corpus level fine grained entity typing using contextual information"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "hierarchical losses and new resources for fine grained entity typing and linking"):
  with members:
    - "attention is all you need"
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "poincar e embeddings for learning hierarchical representations"
    - "constructing datasets for multi hop reading comprehension across documents"
    - "capturing semantic similarity for entity linking with convolutional neural networks"
    - "generalizing to unseen entities and entity pairs with row less universal schema"
    - "complex embeddings for simple link prediction"
    - "classifying relations by ranking with convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "reside improving distantly supervised neural relation extraction using side information"):
  with members:
    - "cesi canonicalizing open knowledge bases using embeddings and side information"
    - "see syntax aware entity embedding for neural relation extraction"
    - "candis coupled attention driven neural distant supervision"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "sequence to sequence learning with neural networks"
    - "speech recognition with deep recurrent neural networks"
    - "distributed representations of words and phrases and their compositionality"

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)