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: "weakly supervised relation extraction by pattern enhanced embedding learning"
  Abstract: Extracting relations from text corpora is an important task in text mining. It becomes particularly challenging when focusing on weakly-supervised relation extraction, that is, utilizing a few relation instances (i.e., a pair of entities and their relation) as seeds to extract more instances from corpora. Existing distributional approaches leverage the corpus-level co-occurrence statistics of entities to predict their relations, and require large number of labeled instances to learn effective relation classifiers. Alternatively, pattern-based approaches perform bootstrapping or apply neural networks to model the local contexts, but still rely on large number of labeled instances to build reliable models. In this paper, we study integrating the distributional and pattern-based methods in a weakly-supervised setting, such that the two types of methods can provide complementary supervision for each other to build an effective, unified model. We propose a novel co-training framework with a distributional module and a pattern module. During training, the distributional module helps the pattern module discriminate between the informative patterns and other patterns, and the pattern module generates some highly-confident instances to improve the distributional module. The whole framework can be effectively optimized by iterating between improving the pattern module and updating the distributional module. We conduct experiments on two tasks: knowledge base completion with text corpora and corpus-level relation extraction. Experimental results prove the effectiveness of our framework in the weakly-supervised setting.

Connected through hyperedge HE-1 (induced by source paper "probabilistic logic neural networks for reasoning"):
  with members:
    - "reasoning visual dialogs with structural and partial observations"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "gmnn graph markov neural networks"
    - "reinforcewalk learning to walk in graph with monte carlo tree search"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "improving knowledge graph embedding using simple constraints"
    - "learning human object interactions by graph parsing neural networks"
    - "m walk learning to walk over graphs using monte carlo tree search"

Connected through hyperedge HE-2 (induced by source paper "structured minimally supervised learning for neural relation extraction"):
  with members:
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "combining distant and direct supervision for neural relation extraction"
    - "event representations with tensor based compositions"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "globally normalized transition based neural networks"
    - "neural architectures for named entity recognition"
    - "noise mitigation for neural entity typing and relation extraction"
    - "multilingual relation extraction using compositional universal schema"

Connected through hyperedge HE-3 (induced by source paper "nero a neural rule grounding framework for label efficient relation extraction"):
  with members:
    - "learning dual retrieval module for semi supervised relation extraction"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "attention guided graph convolutional networks for relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "joint bootstrapping machines for high confidence relation extraction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "metapad meta pattern discovery from massive text corpora"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

Connected through hyperedge HE-4 (induced by source paper "uncover the ground truth relations in distant supervision a neural expectation maximization framework"):
  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"
    - "learning with noise enhance distantly supervised relation extraction with dynamic transition matrix"
    - "neural expectation maximization"
    - "jointly extracting relations with class ties via effective deep ranking"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "classifying relations by ranking with convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "hiexpan task guided taxonomy construction by hierarchical tree expansion"):
  with members:
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "end to end reinforcement learning for automatic taxonomy induction"
    - "metapad meta pattern discovery from massive text corpora"
    - "taxonomy induction using hypernym subsequences"
    - "automated phrase mining from massive text corpora"
    - "pte predictive text embedding through large scale heterogeneous text networks"
    - "line large scale information network embedding"
    - "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)