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 using word and entity based attention"
  Abstract: Relation extraction is the problem of classifying the relationship between two entities in a given sentence. Distant Supervision (DS) is a popular technique for developing relation extractors starting with limited supervision. We note that most of the sentences in the distant supervision relation extraction setting are very long and may benefit from word attention for better sentence representation. Our contributions in this paper are threefold. Firstly, we propose two novel word attention models for distantly- supervised relation extraction: (1) a Bi-directional Gated Recurrent Unit (Bi-GRU) based word attention model (BGWA), (2) an entity-centric attention model (EA), and (3) a combination model which combines multiple complementary models using weighted voting method for improved relation extraction. Secondly, we introduce GDS, a new distant supervision dataset for relation extraction. GDS removes test data noise present in all previous distant- supervision benchmark datasets, making credible automatic evaluation possible. Thirdly, through extensive experiments on multiple real-world datasets, we demonstrate the effectiveness of the proposed methods.

Connected through hyperedge HE-1 (induced by source paper "neural graph embedding methods for natural language processing"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "confidence based graph convolutional networks for semi supervised learning"
    - "hypernetwork knowledge graph embeddings"
    - "cesi canonicalizing open knowledge bases using embeddings and side information"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "lovasz convolutional networks"
    - "representation learning on graphs with jumping knowledge networks"
    - "graph to sequence learning using gated graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "amused a multi stream vector representation method for use in natural dialogue"):
  with members:
    - "cesi canonicalizing open knowledge bases using embeddings and side information"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "complex sequential question answering towards learning to converse over linked question answer pairs with a knowledge graph"
    - "a knowledge grounded neural conversation model"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "effective modeling of encoder decoder architecture for joint entity and relation extraction"):
  with members:
    - "distant supervision relation extraction with intra bag and inter bag attentions"
    - "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"
    - "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-4 (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"
    - "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-5 (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 with neural noise converter and conditional optimal selector"
    - "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-6 (induced by source paper "ipre a dataset for inter personal relationship extraction"):
  with members:
    - "attention based capsule networks with dynamic routing for relation extraction"
    - "see syntax aware entity embedding for neural relation extraction"
    - "reinforcement learning for relation classification from noisy data"
    - "adam a method for stochastic optimization"
    - "speech recognition with deep recurrent neural networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "distant supervision relation extraction with intra bag and inter bag attentions"):
  with members:
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "robust distant supervision relation extraction via deep reinforcement learning"
    - "reinforcement learning for relation classification from noisy data"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "attention based capsule networks with dynamic routing for relation extraction"):
  with members:
    - "investigating capsule networks with dynamic routing for text classification"
    - "ensemble neural relation extraction with adaptive boosting"
    - "information aggregation via dynamic routing for sequence encoding"
    - "see syntax aware entity embedding for neural relation extraction"
    - "cross sentence n ary relation extraction with graph lstms"
    - "dynamic routing between capsules"
    - "a structured self attentive sentence embedding"
    - "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)