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: "idk cascades fast deep learning by learning not to overthink"
  Abstract: Advances in deep learning have led to substantial increases in prediction accuracy but have been accompanied by increases in the cost of rendering predictions. We conjecture that fora majority of real-world inputs, the recent advances in deep learning have created models that effectively "overthink" on simple inputs. In this paper, we revisit the classic question of building model cascades that primarily leverage class asymmetry to reduce cost. We introduce the "I Don't Know"(IDK) prediction cascades framework, a general framework to systematically compose a set of pre-trained models to accelerate inference without a loss in prediction accuracy. We propose two search based methods for constructing cascades as well as a new cost-aware objective within this framework. The proposed IDK cascade framework can be easily adopted in the existing model serving systems without additional model re-training. We evaluate the proposed techniques on a range of benchmarks to demonstrate the effectiveness of the proposed framework.

Connected through hyperedge HE-1 (induced by source paper "model slicing for supporting complex analytics with elastic inference cost and resource constraints"):
  with members:
    - "isbnet instance aware selective branching networks"
    - "group normalization"
    - "slimmable neural networks"
    - "skipnet learning dynamic routing in convolutional networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning efficient convolutional networks through network slimming"
    - "learning anytime predictions in neural networks via adaptive loss balancing"

Connected through hyperedge HE-2 (induced by source paper "parity models a general framework for coding based resilience in ml inference"):
  with members:
    - "collage inference tolerating stragglers in distributed neural network inference using coding"
    - "pretzel opening the black box of machine learning prediction serving systems"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning a code machine learning for approximate non linear coded computation"
    - "leveraging coding techniques for speeding up distributed computing"
    - "speeding up distributed machine learning using codes"
    - "rafiki machine learning as an analytics service system"
    - "tvm an automated end to end optimizing compiler for deep learning"

Connected through hyperedge HE-3 (induced by source paper "tafe net task aware feature embeddings for low shot learning"):
  with members:
    - "attributes as operators"
    - "low shot learning from imaginary data"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "skipnet learning dynamic routing in convolutional networks"
    - "modulating early visual processing by language"
    - "cycada cycle consistent adversarial domain adaptation"
    - "prototypical networks for few shot learning"
    - "blockdrop dynamic inference paths in residual networks"

Connected through hyperedge HE-4 (induced by source paper "willump a statistically aware end to end optimizer for machine learning inference"):
  with members:
    - "accelerating deep learning inference via freezing"
    - "gradient boosted feature selection"
    - "parity models a general framework for coding based resilience in ml inference"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "focus querying large video datasets with low latency and low cost"
    - "tensorflow serving flexible high performance ml serving"
    - "noscope optimizing neural network queries over video at scale"
    - "clipper a low latency online prediction serving system"

Connected through hyperedge HE-5 (induced by source paper "pay attention to convolution filters towards fast and accurate fine grained transfer learning"):
  with members:
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"
    - "what makes imagenet good for transfer learning"
    - "pruning filters for efficient convnets"
    - "subset feature learning for fine grained category classification"
    - "deep residual learning for image recognition"
    - "structured pruning of deep convolutional neural networks"
    - "rethinking the inception architecture for computer vision"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-6 (induced by source paper "tafe net task aware feature embeddings for efficient learning and inference"):
  with members:
    - "attributes as operators"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"
    - "low shot learning from imaginary data"
    - "skipnet learning dynamic routing in convolutional networks"
    - "modulating early visual processing by language"
    - "data driven sparse structure selection for deep neural networks"
    - "cycada cycle consistent adversarial domain adaptation"
    - "deep layer aggregation"

Connected through hyperedge HE-7 (induced by source paper "shallow deep networks understanding and mitigating network overthinking"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "greedy layerwise learning can scale to imagenet"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "one bit matters understanding adversarial examples as the abuse of redundancy"
    - "on calibration of modern neural networks"
    - "adaptive neural networks for efficient inference"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-8 (induced by source paper "inferline ml inference pipeline composition framework"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "device placement optimization with reinforcement learning"
    - "clipper a low latency online prediction serving system"
    - "deep compositional question answering with neural module networks"
    - "deep residual learning for image recognition"
    - "ask your neurons a neural based approach to answering questions about images"
    - "caffe convolutional architecture for fast feature embedding"
    - "a contextual bandit approach to personalized news article recommendation"

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)