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: "identifying individual facial expressions by deconstructing a neural network"
  Abstract: This paper focuses on the problem of explaining predictions of psychological attributes such as attractiveness, happiness, confidence and intelligence from face photographs using deep neural networks. Since psychological attribute datasets typically suffer from small sample sizes, we apply transfer learning with two base models to avoid overfitting. These models were trained on an age and gender prediction task, respectively. Using a novel explanation method we extract heatmaps that highlight the parts of the image most responsible for the prediction. We further observe that the explanation method provides important insights into the nature of features of the base model, which allow one to assess the aptitude of the base model for a given transfer learning task. Finally, we observe that the multiclass model is more feature rich than its binary counterpart. The experimental evaluation is performed on the 2222 images from the 10k US faces dataset containing psychological attribute labels as well as on a subset of KDEF images.

Connected through hyperedge HE-1 (induced by source paper "finding interesting frames in deep video analytics a top k approach"):
  with members:
    - "blazeit optimizing declarative aggregation and limit queries for neural network based video analytics"
    - "yolov3 an incremental improvement"
    - "focus querying large video datasets with low latency and low cost"
    - "scanner efficient video analysis at scale"
    - "occlusion aware hand pose estimation using hierarchical mixture density network"
    - "face detection using deep learning an improved faster rcnn approach"
    - "abnormality detection and localization in chest x rays using deep convolutional neural networks"
    - "noscope optimizing neural network queries over video at scale"

Connected through hyperedge HE-2 (induced by source paper "unmasking clever hans predictors and assessing what machines really learn"):
  with members:
    - "deep neural networks for no reference and full reference image quality assessment"
    - "methods for interpreting and understanding deep neural networks"
    - "explaining the unique nature of individual gait patterns with deep learning"
    - "explaining recurrent neural network predictions in sentiment analysis"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "visualizing and understanding atari agents"
    - "what is relevant in a text document an interpretable machine learning approach"
    - "socially aware motion planning with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "methods for interpreting and understanding deep neural networks"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "self normalizing neural networks"
    - "explaining recurrent neural network predictions in sentiment analysis"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "the shattered gradients problem if resnets are the answer then what is the question"
    - "what is relevant in a text document an interpretable machine learning approach"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"

Connected through hyperedge HE-4 (induced by source paper "how do convolutional neural networks learn design"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "explaining recurrent neural network predictions in sentiment analysis"
    - "east an efficient and accurate scene text detector"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "what is relevant in a text document an interpretable machine learning approach"
    - "grad cam improved visual explanations for deep convolutional networks"
    - "judging a book by its cover"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-5 (induced by source paper "what is relevant in a text document an interpretable machine learning approach"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "explaining recurrent neural network predictions in sentiment analysis"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "explaining predictions of non linear classifiers in nlp"
    - "why should i trust you explaining the predictions of any classifier"
    - "character level convolutional networks for text classification"
    - "visualizing and understanding neural models in nlp"
    - "analyzing classifiers fisher vectors and deep neural networks"

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)