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: "min entropy latent model for weakly supervised object detection"
  Abstract: Weakly supervised object detection is a challenging task when provided with image category supervision but required to learn, at the same time, object locations and object detectors. The inconsistency between the weak supervision and learning objectives introduces significant randomness to object locations and ambiguity to detectors. In this paper, a min-entropy latent model (MELM) is proposed for weakly supervised object detection. Min-entropy serves as a model to learn object locations and a metric to measure the randomness of object localization during learning. It aims to principally reduce the variance of learned instances and alleviate the ambiguity of detectors. MELM is decomposed into three components including proposal clique partition, object clique discovery, and object localization. MELM is optimized with a recurrent learning algorithm, which leverages continuation optimization to solve the challenging non-convexity problem. Experiments demonstrate that MELM significantly improves the performance of weakly supervised object detection, weakly supervised object localization, and image classification, against the state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised instance segmentation by deep community learning"):
  with members:
    - "weakly supervised learning of instance segmentation with inter pixel relations"
    - "weakly supervised object detection with segmentation collaboration"
    - "frame to frame aggregation of active regions in web videos for weakly supervised semantic segmentation"
    - "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"
    - "where are the masks instance segmentation with image level supervision"
    - "object counting and instance segmentation with image level supervision"
    - "weakly supervised instance segmentation using class peak response"
    - "group normalization"

Connected through hyperedge HE-2 (induced by source paper "ehsod cam guided end to end hybrid supervised object detection with cascade refinement"):
  with members:
    - "baod budget aware object detection"
    - "weakly supervised object detection with segmentation collaboration"
    - "object aware instance labeling for weakly supervised object detection"
    - "region proposal by guided anchoring"
    - "revisiting feature alignment for one stage object detection"
    - "feature selective anchor free module for single shot object detection"
    - "c mil continuation multiple instance learning for weakly supervised object detection"
    - "rethinking imagenet pre training"

Connected through hyperedge HE-3 (induced by source paper "in defense of graph inference algorithms for weakly supervised object localization"):
  with members:
    - "learning to find common objects across few image collections"
    - "c mil continuation multiple instance learning for weakly supervised object detection"
    - "note rcnn noise tolerant ensemble rcnn for semi supervised object detection"
    - "dissimilarity coefficient based weakly supervised object detection"
    - "sniper efficient multi scale training"
    - "focal loss for dense object detection"
    - "revisiting knowledge transfer for training object class detectors"
    - "weakly supervised object localization with multi fold multiple instance learning"

Connected through hyperedge HE-4 (induced by source paper "attention on attention for image captioning"):
  with members:
    - "c mil continuation multiple instance learning for weakly supervised object detection"
    - "less is more picking informative frames for video captioning"
    - "recurrent fusion network for image captioning"
    - "auto encoding scene graphs for image captioning"
    - "exploring visual relationship for image captioning"
    - "mutan multimodal tucker fusion for visual question answering"
    - "non local neural networks"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "weakly supervised object localization using min max entropy an interpretable framework"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "constrained cnn losses for weakly supervised segmentation"
    - "visual interpretability for deep learning a survey"
    - "dropblock a regularization method for convolutional networks"
    - "attention based deep multiple instance learning"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "adversarial complementary learning for weakly supervised object localization"

Connected through hyperedge HE-6 (induced by source paper "cap2det learning to amplify weak caption supervision for object detection"):
  with members:
    - "equal but not the same understanding the implicit relationship between persuasive images and text"
    - "neural baby talk"
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "deep learning for generic object detection a survey"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "pcl proposal cluster learning for weakly supervised object detection"
    - "artistic object recognition by unsupervised style adaptation"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"

Connected through hyperedge HE-7 (induced by source paper "min max entropy for weakly supervised pointwise localization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "constrained cnn losses for weakly supervised segmentation"
    - "visual interpretability for deep learning a survey"
    - "dropblock a regularization method for convolutional networks"
    - "attention based deep multiple instance learning"
    - "adversarial complementary learning for weakly supervised object localization"
    - "tell me where to look guided attention inference network"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"

Connected through hyperedge HE-8 (induced by source paper "curved text detection in natural scene images with semi and weakly supervised learning"):
  with members:
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "scene text detection and recognition the deep learning era"
    - "mask textspotter an end to end trainable neural network for spotting text with arbitrary shapes"
    - "towards end to end text spotting with convolutional recurrent neural networks"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "wordsup exploiting word annotations for character based text detection"
    - "total text a comprehensive dataset for scene text detection and recognition"
    - "east an efficient and accurate scene text detector"

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)