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: "distant supervision relation extraction with intra bag and inter bag attentions"
  Abstract: This paper presents a neural relation extraction method to deal with the noisy training data generated by distant supervision. Previous studies mainly focus on sentence-level de-noising by designing neural networks with intra-bag attentions. In this paper, both intra-bag and inter-bag attentions are considered in order to deal with the noise at sentence-level and bag-level respectively. First, relation-aware bag representations are calculated by weighting sentence embeddings using intra-bag attentions. Here, each possible relation is utilized as the query for attention calculation instead of only using the target relation in conventional methods. Furthermore, the representation of a group of bags in the training set which share the same relation label is calculated by weighting bag representations using a similarity-based inter-bag attention module. Finally, a bag group is utilized as a training sample when building our relation extractor. Experimental results on the New York Times dataset demonstrate the effectiveness of our proposed intra-bag and inter-bag attention modules. Our method also achieves better relation extraction accuracy than state-of-the-art methods on this dataset.

Connected through hyperedge HE-1 (induced by source paper "effective modeling of encoder decoder architecture for joint entity and relation extraction"):
  with members:
    - "effective attention modeling for neural relation extraction"
    - "end to end neural relation extraction using deep biaffine attention"
    - "attention guided graph convolutional networks for relation extraction"
    - "a question focused multi factor attention network for question answering"
    - "improving distantly supervised relation extraction using word and entity based attention"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "deep graph convolutional encoders for structured data to text generation"
    - "a hierarchical framework for relation extraction with reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "effective attention modeling for neural relation extraction"):
  with members:
    - "attention guided graph convolutional networks for relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "improving distantly supervised relation extraction with neural noise converter and conditional optimal selector"
    - "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-3 (induced by source paper "self attention enhanced selective gate with entity aware embedding for distantly supervised relation extraction"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "learning to propagate for graph meta learning"
    - "an empirical study of spatial attention mechanisms in deep networks"
    - "ssa cnn semantic self attention cnn for pedestrian detection"
    - "combining distant and direct supervision for neural relation extraction"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "multi level structured self attentions for distantly supervised relation extraction"
    - "tensorized self attention efficiently modeling pairwise and global dependencies together"

Connected through hyperedge HE-4 (induced by source paper "are noisy sentences useless for distant supervised relation extraction"):
  with members:
    - "distant supervision for relation extraction with linear attenuation simulation and non iid relevance embedding"
    - "improving distantly supervised relation extraction with neural noise converter and conditional optimal selector"
    - "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)