human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "domain aware dynamic networks"):
  Abstract: Deep neural networks with more parameters and FLOPs have higher capacity and generalize better to diverse domains. But to be deployed on edge devices, the model's complexity has to be constrained due to limited compute resource. In this work, we propose a method to improve the model capacity without increasing inference-time complexity. Our method is based on an assumption of data locality: for an edge device, within a short period of time, the input data to the device are sampled from a single domain with relatively low diversity. Therefore, it is possible to utilize a specialized, low-complexity model to achieve good performance in that input domain. To leverage this, we propose Domain-aware Dynamic Network (DDN), which is a high-capacity dynamic network in which each layer contains multiple weights. During inference, based on the input domain, DDN dynamically combines those weights into one single weight that specializes in the given domain. This way, DDN can keep the inference-time complexity low but still maintain a high capacity. Experiments show that without increasing the parameters, FLOPs, and actual latency, DDN achieves up to 2.6\% higher AP50 than a static network on the BDD100K object-detection benchmark.

Includes members:
  Member "test time training for out of distribution generalization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "vision language navigation with self supervised auxiliary reasoning tasks")
  Member "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "performance oriented neural architecture search")
  Member "domain randomization and pyramid consistency simulation to real generalization without accessing target domain data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "madan multi source adversarial domain aggregation network for domain adaptation")
  Member "multi source domain adaptation for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi source domain adaptation in the deep learning era a systematic survey")
  Member "condconv conditionally parameterized convolutions for efficient inference":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "revisiting spatial invariance with low rank local connectivity")
  Member "metapruning meta learning for automatic neural network channel pruning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "localizing multi scale semantic patches for image classification")
  Member "searching for mobilenetv3":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "single path one shot neural architecture search with uniform sampling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gan compression efficient architectures for interactive conditional gans")

Question: Please tell me which class the source paper 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)