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: "pet an eye tracking dataset for animal centric pascal object classes"
  Abstract: We present the Pascal animal classes Eye Tracking database. Our database comprises eye movement recordings compiled from forty users for the bird, cat, cow, dog, horse and sheep {trainval} sets from the VOC 2012 image set. Different from recent eye-tracking databases such as \cite{kiwon_cvpr13_gaze,PapadopoulosCKF14}, a salient aspect of PET is that it contains eye movements recorded for both the free-viewing and visual search task conditions. While some differences in terms of overall gaze behavior and scanning patterns are observed between the two conditions, a very similar number of fixations are observed on target objects for both conditions. As a utility application, we show how feature pooling around fixated locations enables enhanced (animal) object classification accuracy.

Connected through hyperedge HE-1 (induced by source paper "predicting goal directed attention control using inverse reinforcement learning"):
  with members:
    - "finding any waldo with zero shot invariant and efficient visual search"
    - "proximal policy optimization algorithms"
    - "generative adversarial imitation learning"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "microsoft coco common objects in context"

Connected through hyperedge HE-2 (induced by source paper "end to end saliency mapping via probability distribution prediction"):
  with members:
    - "reconciling saliency and object center bias hypotheses in explaining free viewing fixations"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "turkergaze crowdsourcing saliency with webcam based eye tracking"
    - "end to end convolutional network for saliency prediction"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "deep gaze i boosting saliency prediction with feature maps trained on imagenet"

Connected through hyperedge HE-3 (induced by source paper "looking beyond a clever narrative visual context and attention are primary drivers of affect in video advertisements"):
  with members:
    - "multimodal sentiment analysis with word level fusion and reinforcement learning"
    - "yolov3 an incremental improvement"
    - "an eeg based image annotation system"
    - "affect recognition in ads with application to computational advertising"
    - "evaluating content centric vs user centric ad affect recognition"
    - "automatic understanding of image and video advertisements"
    - "object detection can be improved using human derived contextual expectations"
    - "show attend and tell neural image caption generation with visual attention"

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.HC(Human-Computer Interaction)