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: "ecc platform independent energy constrained deep neural network compression via a bilinear regression model"
  Abstract: Many DNN-enabled vision applications constantly operate under severe energy constraints such as unmanned aerial vehicles, Augmented Reality headsets, and smartphones. Designing DNNs that can meet a stringent energy budget is becoming increasingly important. This paper proposes ECC, a framework that compresses DNNs to meet a given energy constraint while minimizing accuracy loss. The key idea of ECC is to model the DNN energy consumption via a novel bilinear regression function. The energy estimate model allows us to formulate DNN compression as a constrained optimization that minimizes the DNN loss function over the energy constraint. The optimization problem, however, has nontrivial constraints. Therefore, existing deep learning solvers do not apply directly. We propose an optimization algorithm that combines the essence of the Alternating Direction Method of Multipliers (ADMM) framework with gradient-based learning algorithms. The algorithm decomposes the original constrained optimization into several subproblems that are solved iteratively and efficiently. ECC is also portable across different hardware platforms without requiring hardware knowledge. Experiments show that ECC achieves higher accuracy under the same or lower energy budget compared to state-of-the-art resource-constrained DNN compression techniques.

Connected through hyperedge HE-1 (induced by source paper "eden enabling energy efficient high performance deep neural network inference using approximate dram"):
  with members:
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "in dram bulk bitwise execution engine"
    - "analyzing and mitigating the impact of permanent faults on a systolic array based neural network accelerator"
    - "amc automl for model compression and acceleration on mobile devices"
    - "high accuracy low precision training"
    - "nullanet training deep neural networks for reduced memory access inference"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "tvm an automated end to end optimizing compiler for deep learning"

Connected through hyperedge HE-2 (induced by source paper "adversarially trained model compression when robustness meets efficiency"):
  with members:
    - "learning sparsity and quantization jointly and automatically for neural network compression via constrained optimization"
    - "adversarial robustness may be at odds with simplicity"
    - "defensive quantization when efficiency meets robustness"
    - "dual dynamic inference enabling more efficient adaptive and controllable deep inference"
    - "towards imperceptible and robust adversarial example attacks against neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "sparse dnns with improved adversarial robustness"
    - "stochastic activation pruning for robust adversarial defense"

Connected through hyperedge HE-3 (induced by source paper "model compression with adversarial robustness a unified optimization framework"):
  with members:
    - "learning sparsity and quantization jointly and automatically for neural network compression via constrained optimization"
    - "adversarial robustness may be at odds with simplicity"
    - "collaborative global local networks for memory efficient segmentation of ultra high resolution images"
    - "defensive quantization when efficiency meets robustness"
    - "dual dynamic inference enabling more efficient adaptive and controllable deep inference"
    - "towards imperceptible and robust adversarial example attacks against neural networks"
    - "sparse dnns with improved adversarial robustness"
    - "stochastic activation pruning for robust adversarial defense"

Connected through hyperedge HE-4 (induced by source paper "legr filter pruning via learned global ranking"):
  with members:
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "adascale towards real time video object detection using adaptive scaling"
    - "transformer xl attentive language models beyond a fixed length context"
    - "approximated oracle filter pruning for destructive cnn width optimization"
    - "habitat a platform for embodied ai research"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"

Connected through hyperedge HE-5 (induced by source paper "energy aware neural architecture optimization with fast splitting steepest descent"):
  with members:
    - "overcoming multi model forgetting"
    - "bag of tricks for image classification with convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "efficient neural architecture search via parameter sharing"

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.LG(Machine Learning)