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: "active self paced learning for cost effective and progressive face identification"
  Abstract: This paper aims to develop a novel cost-effective framework for face identification, which progressively maintains a batch of classifiers with the increasing face images of different individuals. By naturally combining two recently rising techniques: active learning (AL) and self-paced learning (SPL), our framework is capable of automatically annotating new instances and incorporating them into training under weak expert recertification. We first initialize the classifier using a few annotated samples for each individual, and extract image features using the convolutional neural nets. Then, a number of candidates are selected from the unannotated samples for classifier updating, in which we apply the current classifiers ranking the samples by the prediction confidence. In particular, our approach utilizes the high-confidence and low-confidence samples in the self-paced and the active user-query way, respectively. The neural nets are later fine-tuned based on the updated classifiers. Such heuristic implementation is formulated as solving a concise active SPL optimization problem, which also advances the SPL development by supplementing a rational dynamic curriculum constraint. The new model finely accords with the “instructor-student-collaborative” learning mode in human education. The advantages of this proposed framework are two-folds: i) The required number of annotated samples is significantly decreased while the comparable performance is guaranteed. A dramatic reduction of user effort is also achieved over other state-of-the-art active learning techniques. ii) The mixture of SPL and AL effectively improves not only the classifier accuracy compared to existing AL/SPL methods but also the robustness against noisy data. We evaluate our framework on two challenging datasets, which include hundreds of persons under diverse conditions, and demonstrate very promising results. Please find the code of this project at:   http://hcp.sysu.edu.cn/projects/aspl/ .

Connected through hyperedge HE-1 (induced by source paper "low budget unsupervised label query through domain alignment enforcement"):
  with members:
    - "self supervised learning with geometric constraints in monocular video connecting flow depth and camera"
    - "image difficulty curriculum for generative adversarial networks cugan"
    - "unsupervised domain adaptation via regularized conditional alignment"
    - "confidence regularized self training"
    - "beyond sharing weights for deep domain adaptation"
    - "domain generalization by solving jigsaw puzzles"
    - "consistency based semi supervised active learning towards minimizing labeling budget"
    - "self paced deep learning for weakly supervised object detection"

Connected through hyperedge HE-2 (induced by source paper "active learning based classification in automated connected vehicles"):
  with members:
    - "ultra reliable and low latency vehicular communication an active learning approach"
    - "cost effective active learning for deep image classification"
    - "learning with symmetric label noise the importance of being unhinged"

Connected through hyperedge HE-3 (induced by source paper "few example object detection with model communication"):
  with members:
    - "zero shot detection"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "collaborative learning for weakly supervised object detection"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "bridging saliency detection to weakly supervised object detection based on self paced curriculum learning"
    - "learning to segment human by watching youtube"
    - "multiple instance detection network with online instance classifier refinement"

Connected through hyperedge HE-4 (induced by source paper "pseudo labeling curriculum for unsupervised domain adaptation"):
  with members:
    - "diversify and match a domain adaptive representation learning paradigm for object detection"
    - "learning to adapt structured output space for semantic segmentation"
    - "adadepth unsupervised content congruent adaptation for depth estimation"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "multi adversarial domain adaptation"
    - "deep hashing network for unsupervised domain adaptation"
    - "maximum classifier discrepancy for unsupervised domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "learning loss for active learning"):
  with members:
    - "towards human machine cooperation self supervised sample mining for object detection"
    - "a robust and effective approach towards accurate metastasis detection and pn stage classification in breast cancer"
    - "exploring the limits of weakly supervised pretraining"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "representation learning by learning to count"
    - "deep bayesian active learning with image data"
    - "stacked hourglass networks for human pose estimation"
    - "active and continuous exploration with deep neural networks and expected model output changes"

Connected through hyperedge HE-6 (induced by source paper "towards time aware distant supervision for relation extraction"):
  with members:
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "learning with noise enhance distantly supervised relation extraction with dynamic transition matrix"
    - "heterogeneous supervision for relation extraction a representation learning approach"
    - "cotype joint extraction of typed entities and relations with knowledge bases"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "webly supervised learning of convolutional networks"
    - "classifying relations via long short term memory networks along shortest dependency path"
    - "relation classification via recurrent neural network"

Connected through hyperedge HE-7 (induced by source paper "self paced deep regression forests for facial age estimation"):
  with members:
    - "bridgenet a continuity aware probabilistic network for age estimation"
    - "soft ranking label encoding for robust facial age estimation"
    - "self paced multi task clustering"
    - "label distribution learning forests"
    - "deep regression forests for age estimation"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-8 (induced by source paper "self paced multi label learning with diversity"):
  with members:
    - "self paced deep learning for weakly supervised object detection"
    - "multi label learning with global and local label correlation"
    - "self paced multi task learning"
    - "a unified view of multi label performance measures"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "large scale multi label learning with missing labels"

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)