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: "how hard can it be estimating the difficulty of visual search in an image"
  Abstract: We address the problem of estimating image difficulty defined as the human response time for solving a visual search task. We collect human annotations of image difficulty for the PASCAL VOC 2012 data set through a crowd-sourcing platform. We then analyze what human interpretable image properties can have an impact on visual search difficulty, and how accurate are those properties for predicting difficulty. Next, we build a regression model based on deep features learned with state of the art convolutional neural networks and show better results for predicting the ground-truth visual search difficulty scores produced by human annotators. Our model is able to correctly rank about 75% image pairs according to their difficulty score. We also show that our difficulty predictor generalizes well to new classes not seen during training. Finally, we demonstrate that our predicted difficulty scores are useful for weakly supervised object localization (8% improvement) and semi-supervised object classification (1% improvement).

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 "curriculum learning strategies for ir an empirical study on conversation response ranking"):
  with members:
    - "bert with history answer embedding for conversational question answering"
    - "passage re ranking with bert"
    - "introducing mantis a novel multi domain information seeking dialogues dataset"
    - "faq retrieval using query question similarity and bert based query answer relevance"
    - "on the power of curriculum learning in training deep networks"
    - "image difficulty curriculum for generative adversarial networks cugan"
    - "simple applications of bert for ad hoc document retrieval"
    - "a hybrid retrieval generation neural conversation model"

Connected through hyperedge HE-3 (induced by source paper "adaptively denoising proposal collection for weakly supervised object localization"):
  with members:
    - "stacked wasserstein autoencoder"
    - "direct visual inertial odometry with semi dense mapping"
    - "unsupervised deep feature transfer for low resolution image classification"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "towards learning affine invariant representations via data efficient cnns"
    - "mdcn multi scale deep inception convolutional neural networks for efficient object detection"
    - "learning depth from single images with deep neural network embedding focal length"
    - "soft nms improving object detection with one line of code"

Connected through hyperedge HE-4 (induced by source paper "image difficulty curriculum for generative adversarial networks cugan"):
  with members:
    - "on line adaptative curriculum learning for gans"
    - "spectral normalization for generative adversarial networks"
    - "automated curriculum learning for neural networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "pacgan the power of two samples in generative adversarial networks"
    - "the numerics of gans"
    - "multiple instance curriculum learning for weakly supervised object detection"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"

Connected through hyperedge HE-5 (induced by source paper "a convolutional neural network for user identification based on motion sensors"):
  with members:
    - "local learning with deep and handcrafted features for facial expression recognition"
    - "sequential keystroke behavioral biometrics for mobile user identification via multi view deep learning"
    - "attention is all you need"
    - "tensorflow a system for large scale machine learning"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "learning human identity from motion patterns"
    - "hmog new behavioral biometric features for continuous authentication of smartphone users"

Connected through hyperedge HE-6 (induced by source paper "coconet a deep neural network for mapping pixel coordinates to color values"):
  with members:
    - "spatially adaptive image compression using a tiled deep network"
    - "optimizing the trade off between single stage and two stage object detectors using image difficulty prediction"
    - "cas cnn a deep convolutional neural network for image compression artifact suppression"
    - "fast and accurate image super resolution by deep cnn with skip connection and network in network"
    - "learning to inpaint for image compression"
    - "full resolution image compression with recurrent neural networks"
    - "generic 3d convolutional fusion for image restoration"
    - "end to end optimized image compression"

Connected through hyperedge HE-7 (induced by source paper "continuous trade off optimization between fast and accurate deep face detectors"):
  with members:
    - "local learning with deep and handcrafted features for facial expression recognition"
    - "optimizing the trade off between single stage and two stage object detectors using image difficulty prediction"
    - "s 3 fd single shot scale invariant face detector"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "mask r cnn"
    - "face detection with the faster r cnn"
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"

Connected through hyperedge HE-8 (induced by source paper "towards human machine cooperation self supervised sample mining for object detection"):
  with members:
    - "few example object detection with model communication"
    - "self paced deep learning for weakly supervised object detection"
    - "active self paced learning for cost effective and progressive face identification"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "multi task self supervised visual learning"
    - "weakly and semi supervised object detection with expectation maximization algorithm"
    - "r fcn object detection via region based fully convolutional networks"
    - "unsupervised learning of visual representations using videos"

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)