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: "robust distant supervision relation extraction via deep reinforcement learning"
  Abstract: Distant supervision has become the standard method for relation extraction. However, even though it is an efficient method, it does not come at no cost---The resulted distantly-supervised training samples are often very noisy. To combat the noise, most of the recent state-of-the-art approaches focus on selecting one-best sentence or calculating soft attention weights over the set of the sentences of one specific entity pair. However, these methods are suboptimal, and the false positive problem is still a key stumbling bottleneck for the performance. We argue that those incorrectly-labeled candidate sentences must be treated with a hard decision, rather than being dealt with soft attention weights. To do this, our paper describes a radical solution---We explore a deep reinforcement learning strategy to generate the false-positive indicator, where we automatically recognize false positives for each relation type without any supervised information. Unlike the removal operation in the previous studies, we redistribute them into the negative examples. The experimental results show that the proposed strategy significantly improves the performance of distant supervision comparing to state-of-the-art systems.

Connected through hyperedge HE-1 (induced by source paper "deep self supervised representation learning for free hand sketch"):
  with members:
    - "deep learning for free hand sketch a survey"
    - "zero reference deep curve estimation for low light image enhancement"
    - "generative adversarial zero shot relational learning for knowledge graphs"
    - "the devil is in the channels mutual channel loss for fine grained image classification"
    - "multi graph transformer for free hand sketch recognition"
    - "learning for multi model and multi type fitting"
    - "doodle to search practical zero shot sketch based image retrieval"
    - "revisiting self supervised visual representation learning"

Connected through hyperedge HE-2 (induced by source paper "dialog state tracking with reinforced data augmentation"):
  with members:
    - "improving dialogue state tracking by discerning the relevant context"
    - "deep communicating agents for abstractive summarization"
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "reinforced co training"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "towards universal dialogue state tracking"
    - "data augmentation for spoken language understanding via joint variational generation"
    - "banditsum extractive summarization as a contextual bandit"

Connected through hyperedge HE-3 (induced by source paper "improving neural relation extraction with implicit mutual relations"):
  with members:
    - "a walk based model on entity graphs for relation extraction"
    - "predicting semantic relations using global graph properties"
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "improving distantly supervised relation extraction using word and entity based attention"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "reinforcement learning for relation classification from noisy data"
    - "neural relation extraction via inner sentence noise reduction and transfer learning"

Connected through hyperedge HE-4 (induced by source paper "docred a large scale document level relation extraction dataset"):
  with members:
    - "a walk based model on entity graphs for relation extraction"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation"
    - "improving the transformer translation model with document level context"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "fever a large scale dataset for fact extraction and verification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "cross sentence n ary relation extraction with graph lstms"

Connected through hyperedge HE-5 (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"
    - "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"
    - "neural relation extraction via inner sentence noise reduction and transfer learning"

Connected through hyperedge HE-6 (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"
    - "reinforcement learning for relation classification from noisy data"
    - "weakly supervised relation extraction by pattern enhanced embedding learning"
    - "learning with noise enhance distantly supervised relation extraction with dynamic transition matrix"
    - "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-7 (induced by source paper "copymtl copy mechanism for joint extraction of entities and relations with multi task learning"):
  with members:
    - "a walk based model on entity graphs for relation extraction"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "a hierarchical framework for relation extraction with reinforcement learning"
    - "global normalization of convolutional neural networks for joint entity and relation classification"
    - "self normalizing neural networks"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "fine tune bert for docred with two step process"):
  with members:
    - "docred a large scale document level relation extraction dataset"
    - "extracting multiple relations in one pass with pre trained transformers"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "improved neural relation detection for knowledge base question answering"
    - "classifying relations by ranking 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)