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: "multi level structured self attentions for distantly supervised relation extraction"
  Abstract: Attention mechanisms are often used in deep neural networks for distantly supervised relation extraction (DS-RE) to distinguish valid from noisy instances. However, traditional 1-D vector attention models are insufficient for the learning of different contexts in the selection of valid instances to predict the relationship for an entity pair. To alleviate this issue, we propose a novel multi-level structured (2-D matrix) self-attention mechanism for DS-RE in a multi-instance learning (MIL) framework using bidirectional recurrent neural networks. In the proposed method, a structured word-level self-attention mechanism learns a 2-D matrix where each row vector represents a weight distribution for different aspects of an instance regarding two entities. Targeting the MIL issue, the structured sentence-level attention learns a 2-D matrix where each row vector represents a weight distribution on selection of different valid in-stances. Experiments conducted on two publicly available DS-RE datasets show that the proposed framework with a multi-level structured self-attention mechanism significantly outperform state-of-the-art baselines in terms of PR curves, P@N and F1 measures.

Connected through hyperedge HE-1 (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-2 (induced by source paper "improving relation extraction with knowledge attention"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "position aware self attention with relative positional encodings for slot filling"
    - "on2vec embedding based relation prediction for ontology population"
    - "improved neural relation detection for knowledge base question answering"
    - "neural natural language inference models enhanced with external knowledge"
    - "attention is all you need"

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 "self attention enhanced selective gate with entity aware embedding for distantly supervised relation extraction"):
  with members:
    - "distant supervision relation extraction with intra bag and inter bag attentions"
    - "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"
    - "tensorized self attention efficiently modeling pairwise and global dependencies together"

Connected through hyperedge HE-5 (induced by source paper "an introductory survey on attention mechanisms in nlp problems"):
  with members:
    - "self attention with relative position representations"
    - "dynamic meta embeddings for improved sentence representations"
    - "mars memory attention aware recommender system"
    - "topic memory networks for short text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "structured attention networks"
    - "a nested attention neural hybrid model for grammatical error correction"
    - "attention is all you need"

Connected through hyperedge HE-6 (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"
    - "neural relation extraction via inner sentence noise reduction and transfer learning"
    - "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)