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: "learning dual retrieval module for semi supervised relation extraction"
  Abstract: Relation extraction is an important task in structuring content of text data, and becomes especially challenging when learning with weak supervision---where only a limited number of labeled sentences are given and a large number of unlabeled sentences are available. Most existing work exploits unlabeled data based on the ideas of self-training (i.e., bootstrapping a model) and multi-view learning (e.g., ensembling multiple model variants). However, these methods either suffer from the issue of semantic drift, or do not fully capture the problem characteristics of relation extraction. In this paper, we leverage a key insight that retrieving sentences expressing a relation is a dual task of predicting relation label for a given sentence---two tasks are complementary to each other and can be optimized jointly for mutual enhancement. To model this intuition, we propose DualRE, a principled framework that introduces a retrieval module which is jointly trained with the original relation prediction module. In this way, high-quality samples selected by retrieval module from unlabeled data can be used to improve prediction module, and vice versa. Experimental results\footnote{\small Code and data can be found at \url{this https URL}.} on two public datasets as well as case studies demonstrate the effectiveness of the DualRE approach.

Connected through hyperedge HE-1 (induced by source paper "neural snowball for few shot relation learning"):
  with members:
    - "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation"
    - "deep comparison relation columns for few shot learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "prototypical networks for few shot learning"
    - "few shot image recognition by predicting parameters from activations"
    - "self ensembling for visual domain adaptation"
    - "unsupervised open relation extraction"
    - "learning to compare relation network for few shot learning"

Connected through hyperedge HE-2 (induced by source paper "learning to annotate modularizing data augmentation for text classifiers with natural language explanations"):
  with members:
    - "moss end to end dialog system framework with modular supervision"
    - "hierarchical decision making by generating and following natural language instructions"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "generalize symbolic knowledge with neural rule engine"
    - "e snli natural language inference with natural language explanations"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "heterogeneous supervision for relation extraction a representation learning approach"

Connected through hyperedge HE-3 (induced by source paper "nero a neural rule grounding framework for label efficient relation extraction"):
  with members:
    - "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"
    - "weakly supervised relation extraction by pattern enhanced embedding learning"

Connected through hyperedge HE-4 (induced by source paper "uncovering relations for marketing knowledge representation"):
  with members:
    - "logic rules powered knowledge graph embedding"
    - "connecting language and knowledge with heterogeneous representations for neural relation extraction"
    - "revisiting the importance of encoding logic rules in sentiment classification"
    - "reinforcement learning for relation classification from noisy data"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "harnessing deep neural networks with logic rules"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "end to end relation extraction using lstms on sequences and tree structures"

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)