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 about individuals from group statistics"
  Abstract: We propose a new problem formulation which is similar to, but more informative than, the binary multiple-instance learning problem. In this setting, we are given groups of instances (described by feature vectors) along with estimates of the fraction of positively-labeled instances per group. The task is to learn an instance level classifier from this information. That is, we are trying to estimate the unknown binary labels of individuals from knowledge of group statistics. We propose a principled probabilistic model to solve this problem that accounts for uncertainty in the parameters and in the unknown individual labels. This model is trained with an efficient MCMC algorithm. Its performance is demonstrated on both synthetic and real-world data arising in general object recognition.

Connected through hyperedge HE-1 (induced by source paper "learning from label proportions with consistency regularization"):
  with members:
    - "deep multi class learning from label proportions"
    - "mixmatch a holistic approach to semi supervised learning"
    - "deep learning from label proportions for emphysema quantification"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "co training for demographic classification using deep learning from label proportions"
    - "temporal ensembling for semi supervised learning"
    - "wide residual networks"

Connected through hyperedge HE-2 (induced by source paper "deep multi class learning from label proportions"):
  with members:
    - "deep learning from label proportions for emphysema quantification"
    - "co training for demographic classification using deep learning from label proportions"
    - "deep residual learning for image recognition"
    - "learning with a wasserstein loss"

Connected through hyperedge HE-3 (induced by source paper "label propagation for learning with label proportions"):
  with members:
    - "loss factorization weakly supervised learning and label noise robustness"
    - "the sphere challenge activity recognition with multimodal sensor data"

Connected through hyperedge HE-4 (induced by source paper "mining the demographics of political sentiment from twitter using learning from label proportions"):
  with members:
    - "cross language domain adaptation for classifying crisis related short messages"
    - "joint inference of multiple label types in large networks"

Connected through hyperedge HE-5 (induced by source paper "inferring individual attributes from search engine queries and auxiliary information"):
  with members:
    - "large scale distributed semi supervised learning using streaming approximation"

Connected through hyperedge HE-6 (induced by source paper "weakly and semi supervised learning of a dcnn for semantic image segmentation"):
  with members:
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"
    - "convolutional feature masking for joint object and stuff segmentation"
    - "untangling local and global deformations in deep convolutional networks for image classification and sliding window detection"
    - "hypercolumns for object segmentation and fine grained localization"
    - "material recognition in the wild with the materials in context database"
    - "learning deep structured models"
    - "lsda large scale detection through adaptation"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"

Connected through hyperedge HE-7 (induced by source paper "bandit label inference for weakly supervised learning"):
  with members:
    - "on learning to localize objects with minimal supervision"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-8 (induced by source paper "deep multi instance transfer learning"):
  with members:
    - "distributed representations of sentences and documents"
    - "modelling visualising and summarising documents with a single convolutional neural network"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "training restricted boltzmann machines on word observations"
    - "natural language processing almost from scratch"

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)