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: "amodal completion and size constancy in natural scenes"
  Abstract: We consider the problem of enriching current object detection systems with veridical object sizes and relative depth estimates from a single image. There are several technical challenges to this, such as occlusions, lack of calibration data and the scale ambiguity between object size and distance. These have not been addressed in full generality in previous work. Here we propose to tackle these issues by building upon advances in object recognition and using recently created large-scale datasets. We first introduce the task of amodal bounding box completion, which aims to infer the the full extent of the object instances in the image. We then propose a probabilistic framework for learning category-specific object size distributions from available annotations and leverage these in conjunction with amodal completion to infer veridical sizes in novel images. Finally, we introduce a focal length prediction approach that exploits scene recognition to overcome inherent scaling ambiguities and we demonstrate qualitative results on challenging real-world scenes.

Connected through hyperedge HE-1 (induced by source paper "embodied visual recognition"):
  with members:
    - "learning instance segmentation by interaction"
    - "yolov3 an incremental improvement"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "gibson env real world perception for embodied agents"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "active object perceiver recognition guided policy learning for object searching on mobile robots"
    - "neural modular control for embodied question answering"

Connected through hyperedge HE-2 (induced by source paper "how to make a pizza learning a compositional layer based gan model"):
  with members:
    - "recycle gan unsupervised video retargeting"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "composable unpaired image to image translation"
    - "multimodal unsupervised image to image translation"
    - "modular generative adversarial networks"
    - "stochastic video generation with a learned prior"
    - "ganimation anatomically aware facial animation from a single image"

Connected through hyperedge HE-3 (induced by source paper "learning semantics aware distance map with semantics layering network for amodal instance segmentation"):
  with members:
    - "mvtec d2s densely segmented supermarket dataset"
    - "segan segmenting and generating the invisible"
    - "depth image inpainting improving low rank matrix completion with low gradient regularization"
    - "mask r cnn"
    - "pixel level encoding and depth layering for instance level semantic labeling"
    - "ssd single shot multibox detector"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "learning to refine object segments"

Connected through hyperedge HE-4 (induced by source paper "learning to see the invisible end to end trainable amodal instance segmentation"):
  with members:
    - "mvtec d2s densely segmented supermarket dataset"
    - "amodal instance segmentation"
    - "fully convolutional instance aware semantic segmentation"
    - "semantic amodal segmentation"
    - "learning to segment object candidates"
    - "iterative instance segmentation"
    - "deep residual learning for image recognition"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "segan segmenting and generating the invisible"):
  with members:
    - "photographic image synthesis with cascaded refinement networks"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "instance sensitive fully convolutional networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "image to image translation with conditional adversarial networks"
    - "learning to refine object segments"
    - "context encoders feature learning by inpainting"

Connected through hyperedge HE-6 (induced by source paper "visual concepts and compositional voting"):
  with members:
    - "normface l 2 hypersphere embedding for face verification"
    - "von mises fisher mixture model based deep learning application to face verification"
    - "amodal instance segmentation"
    - "doc deep occlusion estimation from a single image"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "unsupervised learning of object semantic parts from internal states of cnns by population encoding"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "detecting parts for action localization"):
  with members:
    - "deep learning for detecting multiple space time action tubes in videos"
    - "learning to track for spatio temporal action localization"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "finding action tubes"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "amodal instance segmentation"):
  with members:
    - "semantic amodal segmentation"
    - "iterative instance segmentation"
    - "instance aware semantic segmentation via multi task network cascades"
    - "conditional random fields as recurrent neural networks"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "convolutional feature masking for joint object and stuff segmentation"
    - "very deep convolutional networks for large scale image recognition"

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)