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: "prediction of search targets from fixations in open world settings"
  Abstract: Previous work on predicting the target of visual search from human fixations only considered closed-world settings in which training labels are available and predictions are performed for a known set of potential targets. In this work we go beyond the state of the art by studying search target prediction in an open-world setting in which we no longer assume that we have fixation data to train for the search targets. We present a dataset containing fixation data of 18 users searching for natural images from three image categories within synthesised image collages of about 80 images. In a closed-world baseline experiment we show that we can predict the correct target image out of a candidate set of five images. We then present a new problem formulation for search target prediction in the open-world setting that is based on learning compatibilities between fixations and potential targets.

Connected through hyperedge HE-1 (induced by source paper "a self validation network for object level human attention estimation"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "scaling egocentric vision the epic kitchens dataset"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "object level visual reasoning in videos"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "attention is all you need"
    - "the kinetics human action video dataset"
    - "instance level salient object segmentation"

Connected through hyperedge HE-2 (induced by source paper "evaluation of appearance based methods and implications for gaze based applications"):
  with members:
    - "forecasting user attention during everyday mobile interactions using device integrated and wearable sensors"
    - "detecting low rapport during natural interactions in small groups from non verbal behaviour"
    - "learning to find eye region landmarks for remote gaze estimation in unconstrained settings"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "learning from simulated and unsupervised images through adversarial training"
    - "it s written all over your face full face appearance based gaze estimation"
    - "rendering of eyes for eye shape registration and gaze estimation"
    - "tabletgaze unconstrained appearance based gaze estimation in mobile tablets"

Connected through hyperedge HE-3 (induced by source paper "recent advances in open set recognition a survey"):
  with members:
    - "open world learning and application to product classification"
    - "large scale long tailed recognition in an open world"
    - "c2ae class conditioned auto encoder for open set recognition"
    - "bayesian semantic instance segmentation in open set world"
    - "learning a neural network based representation for open set recognition"
    - "open category detection with pac guarantees"
    - "open set domain adaptation by backpropagation"
    - "reducing network agnostophobia"

Connected through hyperedge HE-4 (induced by source paper "mpiigaze real world dataset and deep appearance based gaze estimation"):
  with members:
    - "eye tracking for everyone"
    - "labelled pupils in the wild a dataset for studying pupil detection in unconstrained environments"
    - "it s written all over your face full face appearance based gaze estimation"
    - "appearance based gaze estimation in the wild"
    - "rendering of eyes for eye shape registration and gaze estimation"
    - "turkergaze crowdsourcing saliency with webcam based eye tracking"
    - "adam a method for stochastic optimization"
    - "when computer vision gazes at cognition"

Connected through hyperedge HE-5 (induced by source paper "object category understanding via eye fixations on freehand sketches"):
  with members:
    - "analyzing structural characteristics of object category representations from their semantic part distributions"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"
    - "cat2000 a large scale fixation dataset for boosting saliency research"
    - "the cross depiction problem computer vision algorithms for recognising objects in artwork and in photographs"

Connected through hyperedge HE-6 (induced by source paper "semi supervised vocabulary informed learning"):
  with members:
    - "transductive multi view zero shot learning"
    - "towards open world recognition"
    - "evaluation of output embeddings for fine grained image classification"
    - "zero shot recognition with unreliable attributes"
    - "return of the devil in the details delving deep into convolutional nets"
    - "learning distributed word representations for natural logic reasoning"
    - "a unified semantic embedding relating taxonomies and attributes"
    - "zero shot learning by convex combination of semantic embeddings"

Connected through hyperedge HE-7 (induced by source paper "gaze embeddings for zero shot image classification"):
  with members:
    - "seeing with humans gaze assisted neural image captioning"
    - "label embedding for image classification"
    - "latent embeddings for zero shot classification"
    - "evaluation of output embeddings for fine grained image classification"
    - "gazedpm early integration of gaze information in deformable part models"
    - "going deeper with convolutions"
    - "multiple instance reinforcement learning for efficient weakly supervised detection in images"
    - "the secrets of salient object segmentation"

Connected through hyperedge HE-8 (induced by source paper "predicting the category and attributes of visual search targets using deep gaze pooling"):
  with members:
    - "seeing with humans gaze assisted neural image captioning"
    - "gaze embeddings for zero shot image classification"
    - "whittlesearch interactive image search with relative attribute feedback"
    - "gazedpm early integration of gaze information in deformable part models"
    - "learning deep features for discriminative localization"
    - "multiple instance reinforcement learning for efficient weakly supervised detection in images"
    - "the secrets of salient object segmentation"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

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)