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: "contemplating visual emotions understanding and overcoming dataset bias"
  Abstract: While machine learning approaches to visual emotion recognition offer great promise, current methods consider training and testing models on small scale datasets covering limited visual emotion concepts. Our analysis identifies an important but long overlooked issue of existing visual emotion benchmarks in the form of dataset biases. We design a series of tests to show and measure how such dataset biases obstruct learning a generalizable emotion recognition model. Based on our analysis, we propose a webly supervised approach by leveraging a large quantity of stock image data. Our approach uses a simple yet effective curriculum guided training strategy for learning discriminative emotion features. We discover that the models learned using our large scale stock image dataset exhibit significantly better generalization ability than the existing datasets without the manual collection of even a single label. Moreover, visual representation learned using our approach holds a lot of promise across a variety of tasks on different image and video datasets.

Connected through hyperedge HE-1 (induced by source paper "learning de biased representations with biased representations"):
  with members:
    - "similarity of neural network representations revisited"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "towards causal vqa revealing and reducing spurious correlations by invariant and covariant semantic editing"
    - "sunny and dark outside improving answer consistency in vqa through entailed question generation"
    - "don t take the easy way out ensemble based methods for avoiding known dataset biases"
    - "only time can tell discovering temporal data for temporal modeling"
    - "repair removing representation bias by dataset resampling"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"

Connected through hyperedge HE-2 (induced by source paper "priming deep neural networks with synthetic faces for enhanced performance"):
  with members:
    - "virtual training for a real application accurate object robot relative localization without calibration"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "what are the biases in my word embedding"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "robot learning in homes improving generalization and reducing dataset bias"
    - "empirically analyzing the effect of dataset biases on deep face recognition systems"
    - "began boundary equilibrium generative adversarial networks"
    - "examining cnn representations with respect to dataset bias"

Connected through hyperedge HE-3 (induced by source paper "can synthetic faces undo the damage of dataset bias to face recognition and facial landmark detection"):
  with members:
    - "virtual training for a real application accurate object robot relative localization without calibration"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "what are the biases in my word embedding"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "robot learning in homes improving generalization and reducing dataset bias"
    - "empirically analyzing the effect of dataset biases on deep face recognition systems"
    - "inversefacenet deep single shot inverse face rendering from a single image"
    - "unrealcv connecting computer vision to unreal engine"

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)