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: "mixture proportion estimation via kernel embedding of distributions"
  Abstract: Mixture proportion estimation (MPE) is the problem of estimating the weight of a component distribution in a mixture, given samples from the mixture and component. This problem constitutes a key part in many "weakly supervised learning" problems like learning with positive and unlabelled samples, learning with label noise, anomaly detection and crowdsourcing. While there have been several methods proposed to solve this problem, to the best of our knowledge no efficient algorithm with a proven convergence rate towards the true proportion exists for this problem. We fill this gap by constructing a provably correct algorithm for MPE, and derive convergence rates under certain assumptions on the distribution. Our method is based on embedding distributions onto an RKHS, and implementing it only requires solving a simple convex quadratic programming problem a few times. We run our algorithm on several standard classification datasets, and demonstrate that it performs comparably to or better than other algorithms on most datasets.

Connected through hyperedge HE-1 (induced by source paper "towards mixture proportion estimation without irreducibility"):
  with members:
    - "dedpul method for mixture proportion estimation and positive unlabeled classification based on density estimation"
    - "are anchor points really indispensable in label noise learning"
    - "dedpul difference of estimated densities based positive unlabeled learning"
    - "mitigating overfitting in supervised classification from two unlabeled datasets a consistent risk correction approach"
    - "masking a new perspective of noisy supervision"
    - "classification from pairwise similarity and unlabeled data"
    - "positive unlabeled learning with non negative risk estimator"
    - "semi supervised classification based on classification from positive and unlabeled data"

Connected through hyperedge HE-2 (induced by source paper "a variational approach for learning from positive and unlabeled data"):
  with members:
    - "additive margin softmax for face verification"
    - "positive unlabeled learning with non negative risk estimator"
    - "class prior estimation for learning from positive and unlabeled data"
    - "generative adversarial positive unlabelled learning"

Connected through hyperedge HE-3 (induced by source paper "l_dmi an information theoretic noise robust loss function"):
  with members:
    - "max mig an information theoretic approach for joint learning from crowds"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "joint optimization framework for learning with noisy labels"
    - "water from two rocks maximizing the mutual information"
    - "dimensionality driven learning with noisy labels"
    - "learning to reweight examples for robust deep learning"
    - "masking a new perspective of noisy supervision"
    - "using trusted data to train deep networks on labels corrupted by severe noise"

Connected through hyperedge HE-4 (induced by source paper "positive unlabeled compression on the cloud"):
  with members:
    - "dafl data free learning of student networks"
    - "full stack filters to build minimum viable cnns"
    - "revisiting sample selection approach to positive unlabeled learning turning unlabeled data into positive rather than negative"
    - "data free learning of student networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "slimnets an exploration of deep model compression and acceleration"
    - "few sample knowledge distillation for efficient network compression"
    - "knowledge distillation from few samples"

Connected through hyperedge HE-5 (induced by source paper "are anchor points really indispensable in label noise learning"):
  with members:
    - "curriculumnet weakly supervised learning from large scale web images"
    - "joint optimization framework for learning with noisy labels"
    - "dimensionality driven learning with noisy labels"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "learning to reweight examples for robust deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "masking a new perspective of noisy supervision"
    - "exploring generalization in deep learning"

Connected through hyperedge HE-6 (induced by source paper "noise tolerant fair classification"):
  with members:
    - "agnostic federated learning"
    - "fairness risk measures"
    - "training well generalizing classifiers for fairness metrics and other data dependent constraints"
    - "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices"
    - "ifair learning individually fair data representations for algorithmic decision making"
    - "a moral framework for understanding of fair ml through economic models of equality of opportunity"
    - "differentially private fair learning"
    - "fairness through computationally bounded awareness"

Connected through hyperedge HE-7 (induced by source paper "generating relevant counter examples from a positive unlabeled dataset for image classification"):
  with members:
    - "efficient training for positive unlabeled learning"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "generalizable adversarial training via spectral normalization"
    - "dropout gan learning from a dynamic ensemble of discriminators"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "progressive growing of gans for improved quality stability and variation"
    - "positive unlabeled learning with non negative risk estimator"

Connected through hyperedge HE-8 (induced by source paper "scalable evaluation and improvement of document set expansion via neural positive unlabeled learning"):
  with members:
    - "scibert pretrained contextualized embeddings for scientific text"
    - "revisiting sample selection approach to positive unlabeled learning turning unlabeled data into positive rather than negative"
    - "classification from positive unlabeled and biased negative data"
    - "alternate estimation of a classifier and the class prior from positive and unlabeled data"
    - "imbalanced deep learning by minority class incremental rectification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep imbalanced learning for face recognition and attribute prediction"
    - "attention is all you need"

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)