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: "see syntax aware entity embedding for neural relation extraction"
  Abstract: Distant supervised relation extraction is an efficient approach to scale relation extraction to very large corpora, and has been widely used to find novel relational facts from plain text. Recent studies on neural relation extraction have shown great progress on this task via modeling the sentences in low-dimensional spaces, but seldom considered syntax information to model the entities. In this paper, we propose to learn syntax-aware entity embedding for neural relation extraction. First, we encode the context of entities on a dependency tree as sentence-level entity embedding based on tree-GRU. Then, we utilize both intra-sentence and inter-sentence attentions to obtain sentence set-level entity embedding over all sentences containing the focus entity pair. Finally, we combine both sentence embedding and entity embedding for relation classification. We conduct experiments on a widely used real-world dataset and the experimental results show that our model can make full use of all informative instances and achieve state-of-the-art performance of relation extraction.

Connected through hyperedge HE-1 (induced by source paper "relation adversarial network for low resource knowledgegraph completion"):
  with members:
    - "long tail relation extraction via knowledge graph embeddings and graph convolution networks"
    - "multi task feature learning for knowledge graph enhanced recommendation"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "kg bert bert for knowledge graph completion"
    - "ernie enhanced language representation with informative entities"
    - "iteratively learning embeddings and rules for knowledge graph reasoning"
    - "transfer learning for relation extraction via relation gated adversarial learning"
    - "meta relational learning for few shot link prediction in knowledge graphs"

Connected through hyperedge HE-2 (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"
    - "universal language model fine tuning for text classification"
    - "neural relation extraction via inner sentence noise reduction and transfer learning"

Connected through hyperedge HE-3 (induced by source paper "transfer learning for relation extraction via relation gated adversarial learning"):
  with members:
    - "long tail relation extraction via knowledge graph embeddings and graph convolution networks"
    - "adversarial domain adaptation for duplicate question detection"
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "partial adversarial domain adaptation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "partial transfer learning with selective adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "integrating relation constraints with neural relation extractors"):
  with members:
    - "lattice cnns for matching based chinese question answering"
    - "marrying up regular expressions with neural networks a case study for spoken language understanding"
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "encoding implicit relation requirements for relation extraction a joint inference approach"
    - "reinforcement learning for relation classification from noisy data"
    - "improved neural relation detection for knowledge base question answering"
    - "a semantic loss function for deep learning with symbolic knowledge"

Connected through hyperedge HE-5 (induced by source paper "ipre a dataset for inter personal relationship extraction"):
  with members:
    - "improving distantly supervised relation extraction using word and entity based attention"
    - "attention based capsule networks with dynamic routing for 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-6 (induced by source paper "exploiting entity bio tag embeddings and multi task learning for relation extraction with imbalanced data"):
  with members:
    - "a walk based model on entity graphs for relation extraction"
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "global relation embedding for relation extraction"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "classifying relations by ranking with convolutional neural networks"

Connected through hyperedge HE-7 (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"
    - "improving distantly supervised relation extraction using word and entity based attention"
    - "information aggregation via dynamic routing for sequence encoding"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "reside improving distantly supervised neural relation extraction using side information"):
  with members:
    - "cesi canonicalizing open knowledge bases using embeddings and side information"
    - "candis coupled attention driven neural distant supervision"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "noise mitigation for neural entity typing and relation extraction"
    - "sequence to sequence learning with neural networks"
    - "speech recognition with deep recurrent neural networks"
    - "distributed representations of words and phrases and their compositionality"

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)