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: "pu learning for matrix completion"
  Abstract: In this paper, we consider the matrix completion problem when the observations are one-bit measurements of some underlying matrix M, and in particular the observed samples consist only of ones and no zeros. This problem is motivated by modern applications such as recommender systems and social networks where only "likes" or "friendships" are observed. The problem of learning from only positive and unlabeled examples, called PU (positive-unlabeled) learning, has been studied in the context of binary classification. We consider the PU matrix completion problem, where an underlying real-valued matrix M is first quantized to generate one-bit observations and then a subset of positive entries is revealed. Under the assumption that M has bounded nuclear norm, we provide recovery guarantees for two different observation models: 1) M parameterizes a distribution that generates a binary matrix, 2) M is thresholded to obtain a binary matrix. For the first case, we propose a "shifted matrix completion" method that recovers M using only a subset of indices corresponding to ones, while for the second case, we propose a "biased matrix completion" method that recovers the (thresholded) binary matrix. Both methods yield strong error bounds --- if M is n by n, the Frobenius error is bounded as O(1/((1-rho)n), where 1-rho denotes the fraction of ones observed. This implies a sample complexity of O(n\log n) ones to achieve a small error, when M is dense and n is large. We extend our methods and guarantees to the inductive matrix completion problem, where rows and columns of M have associated features. We provide efficient and scalable optimization procedures for both the methods and demonstrate the effectiveness of the proposed methods for link prediction (on real-world networks consisting of over 2 million nodes and 90 million links) and semi-supervised clustering tasks.

Connected through hyperedge HE-1 (induced by source paper "advances in collaborative filtering and ranking"):
  with members:
    - "temporal collaborative ranking via personalized transformer"
    - "graph dna deep neighborhood aware graph encoding for collaborative filtering"
    - "stochastic shared embeddings data driven regularization of embedding layers"
    - "hierarchical gating networks for sequential recommendation"
    - "graph convolutional neural networks for web scale recommender systems"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a call for clarity in reporting bleu scores"

Connected through hyperedge HE-2 (induced by source paper "on exploring undetermined relationships for visual relationship detection"):
  with members:
    - "efficient training for positive unlabeled learning"
    - "shuffle then assemble learning object agnostic visual relationship features"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "vip cnn visual phrase guided convolutional neural network"
    - "detecting and recognizing human object interactions"
    - "neural motifs scene graph parsing with global context"
    - "relation networks for object detection"
    - "detecting visual relationships with deep relational networks"

Connected through hyperedge HE-3 (induced by source paper "ideotrace a framework for ideology tracing with a case study on the 2016 u s presidential election"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "red bots do it better comparative analysis of social bot partisan behavior"
    - "predicting factuality of reporting and bias of news media sources"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "impact of social media posts in real life violence a case study in bangladesh"
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "who falls for online political manipulation"
    - "validation of twitter opinion trends with national polling aggregates hillary clinton vs donald trump"

Connected through hyperedge HE-4 (induced by source paper "binary matrix completion with nonconvex regularizers"):
  with members:
    - "link prediction via matrix completion"
    - "positive unlabeled learning with non negative risk estimator"
    - "large scale low rank matrix learning with nonconvex regularizers"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "matrix completion via max norm constrained optimization"
    - "semi supervised classification based on classification from positive and unlabeled data"
    - "network topology inference from spectral templates"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"

Connected through hyperedge HE-5 (induced by source paper "distantly supervised named entity recognition using positive unlabeled learning"):
  with members:
    - "chinese ner using lattice lstm"
    - "positive unlabeled learning with non negative risk estimator"
    - "empower sequence labeling with task aware neural language model"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"
    - "named entity recognition with bidirectional lstm cnns"
    - "large scale simple question answering with memory networks"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-6 (induced by source paper "graph dna deep neighborhood aware graph encoding for collaborative filtering"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "representation learning on graphs methods and applications"
    - "getting deep recommenders fit bloom embeddings for sparse binary input output networks"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-7 (induced by source paper "efficient training for positive unlabeled learning"):
  with members:
    - "semi supervised classification based on classification from positive and unlabeled data"
    - "theoretical comparisons of learning from positive negative positive unlabeled and negative unlabeled data"
    - "one class classification taxonomy of study and review of techniques"
    - "semi supervised learning with deep generative models"
    - "convex and scalable weakly labeled svms"

Connected through hyperedge HE-8 (induced by source paper "beyond the selected completely at random assumption for learning from positive and unlabeled data"):
  with members:
    - "learning from positive and unlabeled data a survey"
    - "instance dependent pu learning by bayesian optimal relabeling"
    - "positive unlabeled learning with non negative risk estimator"
    - "class prior estimation for learning from positive and unlabeled data"
    - "generative adversarial positive unlabelled learning"
    - "unbiased learning to rank with biased feedback"
    - "recommendations as treatments debiasing learning and evaluation"
    - "mixture proportion estimation via kernel embedding of distributions"

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.LG(Machine Learning)