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: "improving distantly supervised relation extraction with neural noise converter and conditional optimal selector"
  Abstract: Distant supervised relation extraction has been successfully applied to large corpus with thousands of relations. However, the inevitable wrong labeling problem by distant supervision will hurt the performance of relation extraction. In this paper, we propose a method with neural noise converter to alleviate the impact of noisy data, and a conditional optimal selector to make proper prediction. Our noise converter learns the structured transition matrix on logit level and captures the property of distant supervised relation extraction dataset. The conditional optimal selector on the other hand helps to make proper prediction decision of an entity pair even if the group of sentences is overwhelmed by no-relation sentences. We conduct experiments on a widely used dataset and the results show significant improvement over competitive baseline methods.

Connected through hyperedge HE-1 (induced by source paper "effective attention modeling for neural relation extraction"):
  with members:
    - "distant supervision relation extraction with intra bag and inter bag attentions"
    - "attention guided graph convolutional networks for relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "improving distantly supervised relation extraction using word and entity based attention"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "attention is all you need"
    - "improving slot filling performance with attentive neural networks on dependency structures"
    - "bidirectional attention flow for machine comprehension"

Connected through hyperedge HE-2 (induced by source paper "enriching pre trained language model with entity information for relation classification"):
  with members:
    - "semantic relation classification via bidirectional lstm networks with entity aware attention using latent entity typing"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "attention is all you need"
    - "semi supervised sequence tagging with bidirectional language models"
    - "squad 100 000 questions for machine comprehension of text"
    - "classifying relations by ranking with convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "are noisy sentences useless for distant supervised relation extraction"):
  with members:
    - "distant supervision relation extraction with intra bag and inter bag attentions"
    - "distant supervision for relation extraction with linear attenuation simulation and non iid relevance embedding"
    - "reinforcement learning for relation classification from noisy data"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"
    - "unsupervised deep embedding for clustering analysis"

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)