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: "two phase learning for weakly supervised object localization"
  Abstract: Weakly supervised semantic segmentation and localiza- tion have a problem of focusing only on the most important parts of an image since they use only image-level annota- tions. In this paper, we solve this problem fundamentally via two-phase learning. Our networks are trained in two steps. In the first step, a conventional fully convolutional network (FCN) is trained to find the most discriminative parts of an image. In the second step, the activations on the most salient parts are suppressed by inference conditional feedback, and then the second learning is performed to find the area of the next most important parts. By combining the activations of both phases, the entire portion of the tar- get object can be captured. Our proposed training scheme is novel and can be utilized in well-designed techniques for weakly supervised semantic segmentation, salient region detection, and object location prediction. Detailed experi- ments demonstrate the effectiveness of our two-phase learn- ing in each task.

Connected through hyperedge HE-1 (induced by source paper "min entropy latent model for weakly supervised object detection"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "from bow to cnn two decades of texture representation for texture classification"
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "deep learning for generic object detection a survey"
    - "pcl proposal cluster learning for weakly supervised object detection"
    - "label refinery improving imagenet classification through label progression"
    - "deep self taught learning for weakly supervised object localization"
    - "weakly supervised object localization using things and stuff transfer"

Connected through hyperedge HE-2 (induced by source paper "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "adversarial complementary learning for weakly supervised object localization"
    - "robust tumor localization with pyramid grad cam"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "learning to exploit the prior network knowledge for weakly supervised semantic segmentation"):
  with members:
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "adversarial complementary learning for weakly supervised object localization"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "tell me where to look guided attention inference network"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "exploiting saliency for object segmentation from image level labels"
    - "hide and seek forcing a network to be meticulous for weakly supervised object and action localization"

Connected through hyperedge HE-4 (induced by source paper "cnns lstms and attention networks for pathology detection in medical data"):
  with members:
    - "attention u net learning where to look for the pancreas"
    - "attention gated networks for improving ultrasound scan plane detection"
    - "learn to pay attention"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "comparative study of cnn and rnn for natural language processing"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "convolutional recurrent neural networks for electrocardiogram classification"
    - "a gentle tutorial of recurrent neural network with error backpropagation"

Connected through hyperedge HE-5 (induced by source paper "autoloc weakly supervised temporal action localization"):
  with members:
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "predictive corrective networks for action detection"
    - "temporal action detection with structured segment networks"
    - "deep self taught learning for weakly supervised object localization"
    - "untrimmednets for weakly supervised action recognition and detection"

Connected through hyperedge HE-6 (induced by source paper "decoupled spatial neural attention for weakly supervised semantic segmentation"):
  with members:
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "learning multi level region consistency with dense multi label networks for semantic segmentation"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "exploiting saliency for object segmentation from image level labels"
    - "discovering class specific pixels for weakly supervised semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"):
  with members:
    - "incorporating network built in priors in weakly supervised semantic segmentation"
    - "adversarial complementary learning for weakly supervised object localization"
    - "self explanatory deep salient object detection"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "deep self taught learning for weakly supervised object localization"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "learning to segment human by watching youtube"

Connected through hyperedge HE-8 (induced by source paper "learning image representations by completing damaged jigsaw puzzles"):
  with members:
    - "transitive invariance for self supervised visual representation learning"
    - "colorization as a proxy task for visual understanding"
    - "deep self taught learning for weakly supervised object localization"
    - "multi task self supervised visual learning"
    - "look listen and learn"
    - "unsupervised representation learning by sorting sequences"
    - "representation learning by learning to count"
    - "ambient sound provides supervision for visual learning"

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)