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: "neural relation extraction via inner sentence noise reduction and transfer learning"
  Abstract: Extracting relations is critical for knowledge base completion and construction in which distant supervised methods are widely used to extract relational facts automatically with the existing knowledge bases. However, the automatically constructed datasets comprise amounts of low-quality sentences containing noisy words, which is neglected by current distant supervised methods resulting in unacceptable precisions. To mitigate this problem, we propose a novel word-level distant supervised approach for relation extraction. We first build Sub-Tree Parse(STP) to remove noisy words that are irrelevant to relations. Then we construct a neural network inputting the sub-tree while applying the entity-wise attention to identify the important semantic features of relational words in each instance. To make our model more robust against noisy words, we initialize our network with a priori knowledge learned from the relevant task of entity classification by transfer learning. We conduct extensive experiments using the corpora of New York Times(NYT) and Freebase. Experiments show that our approach is effective and improves the area of Precision/Recall(PR) from 0.35 to 0.39 over the state-of-the-art work.

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 "improving distant supervised relation extraction by dynamic neural network"):
  with members:
    - "global textual relation embedding for relational understanding"
    - "fine tuning pre trained transformer language models to distantly supervised relation extraction"
    - "long tail relation extraction via knowledge graph embeddings and graph convolution networks"
    - "cross relation cross bag attention for distantly supervised relation extraction"
    - "distant supervision for relation extraction with linear attenuation simulation and non iid relevance embedding"
    - "multi labeled relation extraction with attentive capsule network"
    - "combining distant and direct supervision for neural relation extraction"
    - "reside improving distantly supervised neural relation extraction using side information"

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"
    - "robust distant supervision relation extraction via deep reinforcement learning"
    - "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"

Connected through hyperedge HE-4 (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-5 (induced by source paper "legal judgment prediction via multi perspective bi feedback network"):
  with members:
    - "interpretable charge predictions for criminal cases learning to generate court views from fact descriptions"
    - "cail2018 a large scale legal dataset for judgment prediction"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "learning to predict charges for criminal cases with legal basis"
    - "exploring the use of text classification in the legal domain"
    - "a survey on multi task learning"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "multi task sequence to sequence learning"

Connected through hyperedge HE-6 (induced by source paper "herding effect based attention for personalized time sync video recommendation"):
  with members:
    - "attention is all you need"
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "multi labeled relation extraction with attentive capsule network"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "ensemble neural relation extraction with adaptive boosting"
    - "automatic severity classification of coronary artery disease via recurrent capsule network"
    - "dynamic routing between capsules"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "combining neural networks and log linear models to improve relation extraction"
    - "classifying relations by ranking with convolutional neural networks"
    - "relation classification via recurrent neural network"

Connected through hyperedge HE-8 (induced by source paper "combining distant and direct supervision for neural relation extraction"):
  with members:
    - "allennlp a deep semantic natural language processing platform"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "zero shot sequence labeling transferring knowledge from sentences to tokens"
    - "multi level structured self attentions for distantly supervised relation extraction"
    - "deep residual learning for weakly supervised relation extraction"
    - "learning with noise enhance distantly supervised relation extraction with dynamic transition matrix"
    - "neural discourse structure for text categorization"
    - "jointly extracting relations with class ties via effective deep ranking"

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)