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: "deeperlab single shot image parser"
  Abstract: We present a single-shot, bottom-up approach for whole image parsing. Whole image parsing, also known as Panoptic Segmentation, generalizes the tasks of semantic segmentation for 'stuff' classes and instance segmentation for 'thing' classes, assigning both semantic and instance labels to every pixel in an image. Recent approaches to whole image parsing typically employ separate standalone modules for the constituent semantic and instance segmentation tasks and require multiple passes of inference. Instead, the proposed DeeperLab image parser performs whole image parsing with a significantly simpler, fully convolutional approach that jointly addresses the semantic and instance segmentation tasks in a single-shot manner, resulting in a streamlined system that better lends itself to fast processing. For quantitative evaluation, we use both the instance-based Panoptic Quality (PQ) metric and the proposed region-based Parsing Covering (PC) metric, which better captures the image parsing quality on 'stuff' classes and larger object instances. We report experimental results on the challenging Mapillary Vistas dataset, in which our single model achieves 31.95% (val) / 31.6% PQ (test) and 55.26% PC (val) with 3 frames per second (fps) on GPU or near real-time speed (22.6 fps on GPU) with reduced accuracy.

Connected through hyperedge HE-1 (induced by source paper "higherhrnet scale aware representation learning for bottom up human pose estimation"):
  with members:
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "deep high resolution representation learning for visual recognition"
    - "pifpaf composite fields for human pose estimation"
    - "deep high resolution representation learning for human pose estimation"
    - "panoptic deeplab a simple strong and fast baseline for bottom up panoptic segmentation"
    - "cornernet detecting objects as paired keypoints"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "simple baselines for human pose estimation and tracking"

Connected through hyperedge HE-2 (induced by source paper "deep affinity net instance segmentation via affinity"):
  with members:
    - "weakly supervised learning of instance segmentation with inter pixel relations"
    - "upsnet a unified panoptic segmentation network"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "panoptic feature pyramid networks"
    - "cornernet detecting objects as paired keypoints"
    - "instance segmentation by deep coloring"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "bshapenet object detection and instance segmentation with bounding shape masks"

Connected through hyperedge HE-3 (induced by source paper "unifying training and inference for panoptic segmentation"):
  with members:
    - "seamless scene segmentation"
    - "panoptic deeplab a simple strong and fast baseline for bottom up panoptic segmentation"
    - "panoptic feature pyramid networks"
    - "ocnet object context network for scene parsing"
    - "learning to fuse things and stuff"
    - "dual attention network for scene segmentation"
    - "non local neural networks"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "spgnet semantic prediction guidance for scene parsing"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "seamless scene segmentation"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "spg net segmentation prediction and guidance network for image inpainting"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "multi scale structure aware network for human pose estimation"

Connected through hyperedge HE-5 (induced by source paper "interlaced sparse self attention for semantic segmentation"):
  with members:
    - "generating long sequences with sparse transformers"
    - "semantic correlation promoted shape variant context for segmentation"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "devil in the details towards accurate single and multiple human parsing"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"
    - "look into person joint body parsing pose estimation network and a new benchmark"

Connected through hyperedge HE-6 (induced by source paper "ssap single shot instance segmentation with affinity pyramid"):
  with members:
    - "panoptic segmentation"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "affinity derivation and graph merge for instance segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "path aggregation network for instance segmentation"
    - "weakly and semi supervised panoptic segmentation"
    - "focal loss for dense object detection"

Connected through hyperedge HE-7 (induced by source paper "seamless scene segmentation"):
  with members:
    - "an end to end network for panoptic segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "panoptic feature pyramid networks"
    - "learning to fuse things and stuff"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "path aggregation network for instance segmentation"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "idd a dataset for exploring problems of autonomous navigation in unconstrained environments"

Connected through hyperedge HE-8 (induced by source paper "spatialflow bridging all tasks for panoptic segmentation"):
  with members:
    - "panoptic segmentation"
    - "retinamask learning to predict masks improves state of the art single shot detection for free"
    - "an end to end network for panoptic segmentation"
    - "region proposal by guided anchoring"
    - "upsnet a unified panoptic segmentation network"
    - "box driven class wise region masking and filling rate guided loss for weakly supervised semantic segmentation"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "hybrid task cascade for instance segmentation"

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)