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: "deep nets what have they ever done for vision"
  Abstract: This is an opinion paper about the strengths and weaknesses of Deep Nets. They are at the center of recent progress on Artificial Intelligence and are of growing importance in Cognitive Science and Neuroscience since they enable the development of computational models that can deal with a large range of visually realistic stimuli and visual tasks. They have clear limitations but they also have enormous successes. There is also gradual, though incomplete, understanding of their inner workings. It seems unlikely that Deep Nets in their current form will be the best long-term solution either for building general purpose intelligent machines or for understanding the mind/brain, but it is likely that many aspects of them will remain. At present Deep Nets do very well on specific types of visual tasks and on specific benchmarked datasets. But Deep Nets are much less general purpose, flexible, and adaptive than the human visual system. Moreover, methods like Deep Nets may run into fundamental difficulties when faced with the enormous complexity of natural images. To illustrate our main points, while keeping the references small, this paper is slightly biased towards work from our group.

Connected through hyperedge HE-1 (induced by source paper "synthetic humans for action recognition from unseen viewpoints"):
  with members:
    - "temporally coherent full 3d mesh human pose recovery from monocular video"
    - "hierarchically learned view invariant representations for cross view action recognition"
    - "learning joint reconstruction of hands and manipulated objects"
    - "learning to train with synthetic humans"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "sim2real transfer learning for 3d pose estimation motion to the rescue"
    - "virtualhome simulating household activities via programs"
    - "human action recognition and prediction a survey"

Connected through hyperedge HE-2 (induced by source paper "actively seeking and learning from live data"):
  with members:
    - "on first order meta learning algorithms"
    - "natural language to structured query generation via meta learning"
    - "deep learning a critical appraisal"
    - "overcoming language priors in visual question answering with adversarial regularization"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "an empirical evaluation of visual question answering for novel objects"
    - "visual question answering as a meta learning task"
    - "one shot visual imitation learning via meta learning"

Connected through hyperedge HE-3 (induced by source paper "identifying model weakness with adversarial examiner"):
  with members:
    - "using videos to evaluate image model robustness"
    - "embodied visual recognition"
    - "meta sim learning to generate synthetic datasets"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "realistic adversarial examples in 3d meshes"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"
    - "black box adversarial attacks with limited queries and information"

Connected through hyperedge HE-4 (induced by source paper "local relation networks for image recognition"):
  with members:
    - "linknet relational embedding for scene graph"
    - "graph r cnn for scene graph generation"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "adversarial logit pairing"
    - "relational inductive biases deep learning and graph networks"
    - "deep learning a critical appraisal"
    - "deformable convnets v2 more deformable better results"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-5 (induced by source paper "latent space factorisation and manipulation via matrix subspace projection"):
  with members:
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "shaping the narrative arc an information theoretic approach to collaborative dialogue"
    - "learning latent subspaces in variational autoencoders"
    - "i revnet deep invertible networks"
    - "dirichlet variational autoencoder for text modeling"
    - "toward controlled generation of text"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "cvae gan fine grained image generation through asymmetric training"

Connected through hyperedge HE-6 (induced by source paper "weakly supervised universal fracture detection in pelvic x rays"):
  with members:
    - "deep learning predicts hip fracture using confounding patient and healthcare variables"
    - "weakly supervised localization and classification of proximal femur fractures"
    - "weakly supervised medical diagnosis and localization from multiple resolutions"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "detecting hip fractures with radiologist level performance using deep neural networks"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "reducing the dilution an analysis of the information sensitiveness of capsule network with a practical solution"):
  with members:
    - "covariant compositional networks for learning graphs"
    - "dynamic routing between capsules"
    - "group equivariant convolutional networks"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-8 (induced by source paper "greedy structure learning of hierarchical compositional models"):
  with members:
    - "learning a hierarchical compositional shape vocabulary for multi class object representation"

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)