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: "multiple instance learning convolutional neural networks for object recognition"
  Abstract: Convolutional Neural Networks (CNN) have demon- strated its successful applications in computer vision, speech recognition, and natural language processing. For object recog- nition, CNNs might be limited by its strict label requirement and an implicit assumption that images are supposed to be target- object-dominated for optimal solutions. However, the labeling procedure, necessitating laying out the locations of target ob- jects, is very tedious, making high-quality large-scale dataset prohibitively expensive. Data augmentation schemes are widely used when deep networks suffer the insufficient training data problem. All the images produced through data augmentation share the same label, which may be problematic since not all data augmentation methods are label-preserving. In this paper, we propose a weakly supervised CNN framework named Multiple Instance Learning Convolutional Neural Networks (MILCNN) to solve this problem. We apply MILCNN framework to object recognition and report state-of-the-art performance on three benchmark datasets: CIFAR10, CIFAR100 and ILSVRC2015 classification dataset.

Connected through hyperedge HE-1 (induced by source paper "amil adversarial multi instance learning for human pose estimation"):
  with members:
    - "semi and weakly supervised human pose estimation"
    - "attention based deep multiple instance learning"
    - "densepose dense human pose estimation in the wild"
    - "dual discriminator generative adversarial nets"
    - "began boundary equilibrium generative adversarial networks"
    - "benchmarking and error diagnosis in multi instance pose estimation"
    - "dynamic routing between capsules"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"

Connected through hyperedge HE-2 (induced by source paper "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"):
  with members:
    - "a survey on deep learning in medical image analysis"
    - "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks"
    - "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "deep instance level hard negative mining model for histopathology images"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "attention based deep multiple instance learning"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "multiple instance learning for heterogeneous images training a cnn for histopathology"
    - "deep learning based assessment of tumor associated stroma for diagnosing breast cancer in histopathology images"
    - "classifying and segmenting microscopy images with deep multiple instance learning"
    - "training region based object detectors with online hard example mining"

Connected through hyperedge HE-4 (induced by source paper "multiple instance learning for heterogeneous images training a cnn for histopathology"):
  with members:
    - "constrained deep weak supervision for histopathology image segmentation"
    - "classifying and segmenting microscopy images with deep multiple instance learning"
    - "patch based convolutional neural network for whole slide tissue image classification"

Connected through hyperedge HE-5 (induced by source paper "cluster based learning from weakly labeled bags in digital pathology"):
  with members:
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-6 (induced by source paper "explaining aviation safety incidents using deep temporal multiple instance learning"):
  with members:
    - "classifying and segmenting microscopy images with deep multiple instance learning"
    - "modeling precursors for event forecasting via nested multi instance learning"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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.CV(Computer Vision and Pattern Recognition)