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: "learning with noise enhance distantly supervised relation extraction with dynamic transition matrix"
  Abstract: Distant supervision significantly reduces human efforts in building training data for many classification tasks. While promising, this technique often introduces noise to the generated training data, which can severely affect the model performance. In this paper, we take a deep look at the application of distant supervision in relation extraction. We show that the dynamic transition matrix can effectively characterize the noise in the training data built by distant supervision. The transition matrix can be effectively trained using a novel curriculum learning based method without any direct supervision about the noise. We thoroughly evaluate our approach under a wide range of extraction scenarios. Experimental results show that our approach consistently improves the extraction results and outperforms the state-of-the-art in various evaluation scenarios.

Connected through hyperedge HE-1 (induced by source paper "dsreg using distant supervision as a regularizer"):
  with members:
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "syntactic scaffolds for semantic structures"
    - "hybrid semi markov crf for neural sequence labeling"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "attention is all you need"
    - "the narrativeqa reading comprehension challenge"

Connected through hyperedge HE-2 (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-3 (induced by source paper "deep ranking based cost sensitive multi label learning for distant supervision relation extraction"):
  with members:
    - "jointly learning semantic parser and natural language generator via dual information maximization"
    - "interpretable charge predictions for criminal cases learning to generate court views from fact descriptions"
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "robust distant supervision relation extraction via deep reinforcement learning"
    - "semi supervised learning for neural keyphrase generation"
    - "encoding implicit relation requirements for relation extraction a joint inference approach"
    - "reinforcement learning for relation classification from noisy data"
    - "denoising distant supervision for relation extraction via instance level adversarial training"

Connected through hyperedge HE-4 (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"
    - "hierarchical losses and new resources for fine grained entity typing and linking"
    - "end to end neural coreference resolution"
    - "snorkel rapid training data creation with weak supervision"
    - "multi level representations for fine grained typing of knowledge base entities"

Connected through hyperedge HE-5 (induced by source paper "uncover the ground truth relations in distant supervision a neural expectation maximization framework"):
  with members:
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "robust distant supervision relation extraction via deep reinforcement learning"
    - "reinforcement learning for relation classification from noisy data"
    - "weakly supervised relation extraction by pattern enhanced embedding learning"
    - "neural expectation maximization"
    - "jointly extracting relations with class ties via effective deep ranking"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "classifying relations by ranking with convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "feature dependent confusion matrices for low resource ner labeling with noisy labels"):
  with members:
    - "multilingual ner transfer for low resource languages"
    - "handling noisy labels for robustly learning from self training data for low resource sequence labeling"
    - "training a neural network in a low resource setting on automatically annotated noisy data"
    - "learning word vectors for 157 languages"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "learning from noisy large scale datasets with minimal supervision"
    - "learning when to trust distant supervision an application to low resource pos tagging using cross lingual projection"
    - "training convolutional networks with noisy labels"

Connected through hyperedge HE-7 (induced by source paper "towards time aware distant supervision for relation extraction"):
  with members:
    - "active self paced learning for cost effective and progressive face identification"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "heterogeneous supervision for relation extraction a representation learning approach"
    - "cotype joint extraction of typed entities and relations with knowledge bases"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "webly supervised learning of convolutional networks"
    - "classifying relations via long short term memory networks along shortest dependency path"
    - "relation classification via recurrent neural network"

Connected through hyperedge HE-8 (induced by source paper "handling noisy labels for robustly learning from self training data for low resource sequence labeling"):
  with members:
    - "training a neural network in a low resource setting on automatically annotated noisy data"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "learning from noisy large scale datasets with minimal supervision"
    - "an overview of multi task learning in deep neural networks"
    - "polyglot distributed word representations for multilingual nlp"
    - "introduction to the conll 2000 shared task chunking"

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)