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: "errata distant supervision for relation extraction with matrix completion"
  Abstract: The essence of distantly supervised relation extraction is that it is an incomplete multi-label classification problem with sparse and noisy features. To tackle the sparsity and noise challenges, we propose solving the classification problem using matrix completion on factorized matrix of minimized rank. We formulate relation classification as completing the unknown labels of testing items (entity pairs) in a sparse matrix that concatenates training and testing textual features with training labels. Our algorithmic framework is based on the assumption that the rank of item-by-feature and item-by-label joint matrix is low. We apply two optimization models to recover the underlying low-rank matrix leveraging the sparsity of feature-label matrix. The matrix completion problem is then solved by the fixed point continuation (FPC) algorithm, which can find the global optimum. Experiments on two widely used datasets with different dimensions of textual features demonstrate that our low-rank matrix completion approach significantly outperforms the baseline and the state-of-the-art methods.

Connected through hyperedge HE-1 (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"
    - "see syntax aware entity embedding for neural relation extraction"
    - "universal language model fine tuning for text classification"

Connected through hyperedge HE-2 (induced by source paper "combining distant and direct supervision for neural relation extraction"):
  with members:
    - "allennlp a deep semantic natural language processing platform"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "zero shot sequence labeling transferring knowledge from sentences to tokens"
    - "neural relation extraction via inner sentence noise reduction and transfer learning"
    - "multi level structured self attentions for distantly supervised relation extraction"
    - "deep residual learning for weakly supervised relation extraction"
    - "learning with noise enhance distantly supervised relation extraction with dynamic transition matrix"
    - "neural discourse structure for text categorization"

Connected through hyperedge HE-3 (induced by source paper "subspace clustering based tag sharing for inductive tag matrix refinement with complex errors"):
  with members:
    - "socializing the semantic gap a comparative survey on image tag assignment refinement and retrieval"
    - "jointly embedding relations and mentions for knowledge population"
    - "probabilistic belief embedding for knowledge base completion"
    - "large margin nearest neighbor embedding for knowledge representation"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "provable inductive matrix completion"
    - "efficient estimation of word representations in vector space"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-4 (induced by source paper "incremental knowledge base construction using deepdive"):
  with members:
    - "from data fusion to knowledge fusion"
    - "logarithmic time updates and queries in probabilistic networks"
    - "feature engineering for knowledge base construction"
    - "linview incremental view maintenance for complex analytical queries"
    - "dimmwitted a study of main memory statistical analytics"
    - "a convolutional neural network for modelling sentences"
    - "adaptive inference on general graphical models"
    - "incrementally maintaining classification using an rdbms"

Connected through hyperedge HE-5 (induced by source paper "jointly embedding relations and mentions for knowledge population"):
  with members:
    - "probabilistic belief embedding for knowledge base completion"
    - "learning embedding representations for knowledge inference on imperfect and incomplete repositories"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "detecting table region in pdf documents using distant supervision"):
  with members:
    - "distant supervision for entity linking"

Connected through hyperedge HE-7 (induced by source paper "learning embedding representations for knowledge inference on imperfect and incomplete repositories"):
  with members:
    - "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)