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: "knowledge adaptation for efficient semantic segmentation"
  Abstract: Both accuracy and efficiency are of significant importance to the task of semantic segmentation. Existing deep FCNs suffer from heavy computations due to a series of high-resolution feature maps for preserving the detailed knowledge in dense estimation. Although reducing the feature map resolution (i.e., applying a large overall stride) via subsampling operations (e.g., pooling and convolution striding) can instantly increase the efficiency, it dramatically decreases the estimation accuracy. To tackle this dilemma, we propose a knowledge distillation method tailored for semantic segmentation to improve the performance of the compact FCNs with large overall stride. To handle the inconsistency between the features of the student and teacher network, we optimize the feature similarity in a transferred latent domain formulated by utilizing a pre-trained autoencoder. Moreover, an affinity distillation module is proposed to capture the long-range dependency by calculating the non-local interactions across the whole image. To validate the effectiveness of our proposed method, extensive experiments have been conducted on three popular benchmarks: Pascal VOC, Cityscapes and Pascal Context. Built upon a highly competitive baseline, our proposed method can improve the performance of a student network by 2.5\% (mIOU boosts from 70.2 to 72.7 on the cityscapes test set) and can train a better compact model with only 8\% float operations (FLOPS) of a model that achieves comparable performances.

Connected through hyperedge HE-1 (induced by source paper "switchable precision neural networks"):
  with members:
    - "adaptative inference cost with convolutional neural mixture models"
    - "universally slimmable networks and improved training techniques"
    - "autoslim towards one shot architecture search for channel numbers"
    - "be your own teacher improve the performance of convolutional neural networks via self distillation"
    - "once for all train one network and specialize it for efficient deployment on diverse hardware platforms"
    - "searching for accurate binary neural architectures"
    - "auxiliary learning for deep multi task learning"
    - "binary ensemble neural network more bits per network or more networks per bit"

Connected through hyperedge HE-2 (induced by source paper "efficient semantic video segmentation with per frame inference"):
  with members:
    - "deep video inpainting"
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "high resolution representations for labeling pixels and regions"
    - "deep high resolution representation learning for human pose estimation"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "dynamic video segmentation network"
    - "deep spatio temporal random fields for efficient video segmentation"

Connected through hyperedge HE-3 (induced by source paper "effective training of convolutional neural networks with low bitwidth weights and activations"):
  with members:
    - "single path one shot neural architecture search with uniform sampling"
    - "precision highway for ultra low precision quantization"
    - "amc automl for model compression and acceleration on mobile devices"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "discrimination aware channel pruning for deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "joint architecture and knowledge distillation in convolutional neural network for offline handwritten chinese text recognition"):
  with members:
    - "flightnns lightweight quantized deep neural networks for fast and accurate inference"
    - "structured knowledge distillation for dense prediction"
    - "hetconv heterogeneous kernel based convolutions for deep cnns"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "writer aware cnn for parsimonious hmm based offline handwritten chinese text recognition"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "attention based fully convolutional network for speech emotion recognition"

Connected through hyperedge HE-5 (induced by source paper "fasterseg searching for faster real time semantic segmentation"):
  with members:
    - "autogan neural architecture search for generative adversarial networks"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "customizable architecture search for semantic segmentation"
    - "partial order pruning for best speed accuracy trade off in neural architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "deep high resolution representation learning for visual recognition"
    - "squeezenas fast neural architecture search for faster semantic segmentation"
    - "deep high resolution representation learning for human pose estimation"

Connected through hyperedge HE-6 (induced by source paper "fcos fully convolutional one stage object detection"):
  with members:
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "feature selective anchor free module for single shot object detection"
    - "deep high resolution representation learning for human pose estimation"
    - "cornernet detecting objects as paired keypoints"
    - "group normalization"
    - "an end to end textspotter with explicit alignment and attention"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-7 (induced by source paper "enforcing geometric constraints of virtual normal for depth prediction"):
  with members:
    - "an evaluation of feature matchers forfundamental matrix estimation"
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "fcos fully convolutional one stage object detection"
    - "deep attention based classification network for robust depth prediction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "rethinking monocular depth estimation with adversarial training"
    - "learning a discriminative feature network for semantic segmentation"
    - "deep ordinal regression network for monocular depth estimation"

Connected through hyperedge HE-8 (induced by source paper "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"):
  with members:
    - "learning a discriminative feature network for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "context encoding for semantic segmentation"
    - "the devil is in the decoder classification regression and gans"
    - "stacked deconvolutional network for semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "bridging category level and instance level semantic image 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)