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: "a convex relaxation for weakly supervised classifiers"
  Abstract: This paper introduces a general multi-class approach to weakly supervised classification. Inferring the labels and learning the parameters of the model is usually done jointly through a block-coordinate descent algorithm such as expectation-maximization (EM), which may lead to local minima. To avoid this problem, we propose a cost function based on a convex relaxation of the soft-max loss. We then propose an algorithm specifically designed to efficiently solve the corresponding semidefinite program (SDP). Empirically, our method compares favorably to standard ones on different datasets for multiple instance learning and semi-supervised learning as well as on clustering tasks.

Connected through hyperedge HE-1 (induced by source paper "more supervision less computation statistical computational tradeoffs in weakly supervised learning"):
  with members:
    - "classification accuracy as a proxy for two sample testing"
    - "on the complexity of random satisfiability problems with planted solutions"

Connected through hyperedge HE-2 (induced by source paper "deep clustering for unsupervised learning of visual features"):
  with members:
    - "transitive invariance for self supervised visual representation learning"
    - "an evaluation of large scale methods for image instance and class discovery"
    - "billion scale similarity search with gpus"
    - "multi task self supervised visual learning"
    - "convnets and imagenet beyond accuracy understanding mistakes and uncovering biases"
    - "representation learning by learning to count"
    - "learning representations for automatic colorization"
    - "ambient sound provides supervision for visual learning"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised object detection in artworks"):
  with members:
    - "do better imagenet models transfer better"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "an implementation of faster rcnn with study for region sampling"
    - "multiple instance learning a survey of problem characteristics and applications"
    - "bam the behance artistic media dataset for recognition beyond photography"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "deeper broader and artier domain generalization"
    - "weakly supervised object localization with multi fold multiple instance learning"

Connected through hyperedge HE-4 (induced by source paper "discrete continuous admm for transductive inference in higher order mrfs"):
  with members:
    - "analysis and optimization of loss functions for multiclass top k and multilabel classification"
    - "a message passing algorithm for the minimum cost multicut problem"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "a new look at reweighted message passing"
    - "secrets of grabcut and kernel k means"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"

Connected through hyperedge HE-5 (induced by source paper "weakly supervised cascaded convolutional networks"):
  with members:
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "ssd single shot multibox detector"
    - "weakly supervised deep detection networks"
    - "fast r cnn"
    - "deepproposal hunting objects by cascading deep convolutional layers"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "constrained convolutional neural networks for weakly supervised segmentation"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-6 (induced by source paper "loss factorization weakly supervised learning and label noise robustness"):
  with members:
    - "learning with symmetric label noise the importance of being unhinged"
    - "making risk minimization tolerant to label noise"
    - "learning reductions that really work"
    - "training convolutional networks with noisy labels"
    - "noise tolerance under risk minimization"
    - "optimization with sparsity inducing penalties"

Connected through hyperedge HE-7 (induced by source paper "convexification of learning from constraints"):
  with members:
    - "learning with submodular functions a convex optimization perspective"

Connected through hyperedge HE-8 (induced by source paper "on learning to localize objects with minimal supervision"):
  with members:
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "convex and scalable weakly labeled svms"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "modeling latent variable uncertainty for loss based learning"
    - "unsupervised discovery of mid level discriminative patches"
    - "optimization with sparsity inducing penalties"

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)