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: "incorporating relation paths in neural relation extraction"
  Abstract: Distantly supervised relation extraction has been widely used to find novel relational facts from plain text. To predict the relation between a pair of two target entities, existing methods solely rely on those direct sentences containing both entities. In fact, there are also many sentences containing only one of the target entities, which provide rich and useful information for relation extraction. To address this issue, we build inference chains between two target entities via intermediate entities, and propose a path-based neural relation extraction model to encode the relational semantics from both direct sentences and inference chains. Experimental results on real-world datasets show that, our model can make full use of those sentences containing only one target entity, and achieves significant and consistent improvements on relation extraction as compared with baselines.

Connected through hyperedge HE-1 (induced by source paper "neural logic machines"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "deepproblog neural probabilistic logic programming"
    - "logical rule induction and theory learning using neural theorem proving"
    - "tunneling neural perception and logic reasoning through abductive learning"
    - "leveraging grammar and reinforcement learning for neural program synthesis"
    - "learning explanatory rules from noisy data"
    - "robustfill neural program learning under noisy i o"
    - "differentiable learning of logical rules for knowledge base reasoning"

Connected through hyperedge HE-2 (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-3 (induced by source paper "dsreg using distant supervision as a regularizer"):
  with members:
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "syntactic scaffolds for semantic structures"
    - "hybrid semi markov crf for neural sequence labeling"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "attention is all you need"
    - "learning with noise enhance distantly supervised relation extraction with dynamic transition matrix"

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"
    - "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-5 (induced by source paper "deep ranking based cost sensitive multi label learning for distant supervision relation extraction"):
  with members:
    - "jointly learning semantic parser and natural language generator via dual information maximization"
    - "interpretable charge predictions for criminal cases learning to generate court views from fact descriptions"
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "robust distant supervision relation extraction via deep reinforcement learning"
    - "semi supervised learning for neural keyphrase generation"
    - "encoding implicit relation requirements for relation extraction a joint inference approach"
    - "reinforcement learning for relation classification from noisy data"
    - "denoising distant supervision for relation extraction via instance level adversarial training"

Connected through hyperedge HE-6 (induced by source paper "connecting the dots document level neural relation extraction with edge oriented graphs"):
  with members:
    - "relation extraction using explicit context conditioning"
    - "document level n ary relation extraction with multiscale representation learning"
    - "a walk based model on entity graphs for relation extraction"
    - "a general framework for information extraction using dynamic span graphs"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "neural relation extraction within and across sentence boundaries"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "reside improving distantly supervised neural relation extraction using side information"

Connected through hyperedge HE-7 (induced by source paper "long tail relation extraction via knowledge graph embeddings and graph convolution networks"):
  with members:
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "reinforcement learning for relation classification from noisy data"
    - "deep residual learning for weakly supervised relation extraction"
    - "graph convolutional networks for classification with a structured label space"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "dependency parsing with dilated iterated graph cnns"
    - "jointly extracting relations with class ties via effective deep ranking"

Connected through hyperedge HE-8 (induced by source paper "graph neural networks with generated parameters for relation extraction"):
  with members:
    - "a walk based model on entity graphs for relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "improving entity linking by modeling latent relations between mentions"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "cross sentence n ary relation extraction with graph lstms"
    - "a simple neural network module for relational reasoning"
    - "scene graph generation by iterative message passing"
    - "neural message passing for quantum chemistry"

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)