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: "zoom better to see clearer human part segmentation with auto zoom net"
  Abstract: Parsing human regions into semantic parts, e.g., body, head and arms etc., from a random natural image is challenging while fundamental for computer vision and widely applicable in industry. One major difficulty to handle such a problem is the high flexibility of scale and location of a human instance and its corresponding parts, making the parsing task either lack of boundary details or suffer from local confusions. To tackle such problems, in this work, we propose the "Auto-Zoom Net" (AZN) for human part parsing, which is a unified fully convolutional neural network structure that: (1) parses each human instance into detailed parts. (2) predicts the locations and scales of human instances and their corresponding parts. In our unified network, the two tasks are mutually beneficial. The score maps obtained for parsing help estimate the locations and scales for human instances and their parts. With the predicted locations and scales, our model "zooms" the region into a right scale to further refine the parsing. In practice, we perform the two tasks iteratively so that detailed human parts are gradually recovered. We conduct extensive experiments over the challenging PASCAL-Person-Part segmentation, and show our approach significantly outperforms the state-of-art parsing techniques especially for instances and parts at small scale. In addition, we perform experiments for horse and cow segmentation and also obtain results which are considerably better than state-of-the-art methods (by over 5%)., which is contribued by the proposed iterative zooming process.

Connected through hyperedge HE-1 (induced by source paper "self correction for human parsing"):
  with members:
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "devil in the details towards accurate single and multiple human parsing"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "ocnet object context network for scene parsing"
    - "instance level human parsing via part grouping network"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "averaging weights leads to wider optima and better generalization"

Connected through hyperedge HE-2 (induced by source paper "nddr cnn layer wise feature fusing in multi task cnn by neural discriminative dimensionality reduction"):
  with members:
    - "multi task learning by deep collaboration and application in facial landmark detection"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "squeeze and excitation networks"
    - "heterogeneous face attribute estimation a deep multi task learning approach"
    - "an overview of multi task learning in deep neural networks"
    - "joint multi person pose estimation and semantic part segmentation"
    - "mask r cnn"

Connected through hyperedge HE-3 (induced by source paper "adaptive temporal encoding network for video instance level human parsing"):
  with members:
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "visual question reasoning on general dependency tree"
    - "dual motion gan for future flow embedded video prediction"
    - "holistic instance level human parsing"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "flow guided feature aggregation for video object detection"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"

Connected through hyperedge HE-4 (induced by source paper "instance level human parsing via part grouping network"):
  with members:
    - "holistic instance level human parsing"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "richer convolutional features for edge detection"
    - "interpretable structure evolving lstm"
    - "rethinking atrous convolution for semantic image segmentation"
    - "mask r cnn"
    - "semantic object parsing with graph lstm"

Connected through hyperedge HE-5 (induced by source paper "look into person joint body parsing pose estimation network and a new benchmark"):
  with members:
    - "active self paced learning for cost effective and progressive face identification"
    - "multi context attention for human pose estimation"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "human pose estimation via convolutional part heatmap regression"
    - "proposal free network for instance level object segmentation"
    - "semantic object parsing with local global long short term memory"

Connected through hyperedge HE-6 (induced by source paper "cross domain human parsing via adversarial feature and label adaptation"):
  with members:
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "person search with natural language description"
    - "self adversarial training for human pose estimation"
    - "be your own prada fashion synthesis with structural coherence"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "joint multi person pose estimation and semantic part segmentation"
    - "hcp a flexible cnn framework for multi label image classification"

Connected through hyperedge HE-7 (induced by source paper "deepskeleton skeleton map for 3d human pose regression"):
  with members:
    - "human pose estimation using global and local normalization"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "human pose regression by combining indirect part detection and contextual information"
    - "compositional human pose regression"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "pose2instance harnessing keypoints for person instance segmentation"
    - "multi context attention for human pose estimation"

Connected through hyperedge HE-8 (induced by source paper "scalenet guiding object proposal generation in supermarkets and beyond"):
  with members:
    - "unrealcv connecting computer vision to unreal engine"
    - "researchdoom and cocodoom learning computer vision with games"
    - "instance sensitive fully convolutional networks"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "scale aware pixelwise object proposal networks"
    - "ssd single shot multibox detector"
    - "learning to refine object segments"
    - "what makes for effective detection proposals"

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)