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: "pruned and structurally sparse neural networks"
  Abstract: Advances in designing and training deep neural networks have led to the principle that the large and deeper a network is, the better it can perform. As a result, computational resources have become a key limiting factor in achieving better performance. One strategy to improve network capabilities while decreasing computation required is to replace dense fully-connected and convolutional layers with sparse layers. In this paper we experiment with training on sparse neural network topologies. First, we test pruning-based sparse topologies, which use a network topology obtained by initially training a dense network and then pruning low-weight connections. Second, we test RadiX-Nets, a class of sparse network structures with proven connectivity and sparsity properties. Results show that compared to dense topologies, sparse structures show promise in training potential but also can exhibit highly nonlinear convergence, which merits further study.

Connected through hyperedge HE-1 (induced by source paper "quantized reinforcement learning quarl"):
  with members:
    - "air learning an ai research platform for algorithm hardware benchmarking of autonomous aerial robots"
    - "defensive quantization when efficiency meets robustness"
    - "model compression via distillation and quantization"
    - "per tensor fixed point quantization of the back propagation algorithm"
    - "sbnet sparse blocks network for fast inference"
    - "relaxed quantization for discretized neural networks"
    - "learning to drive in a day"
    - "deep reinforcement learning a brief survey"

Connected through hyperedge HE-2 (induced by source paper "a comparative study of neural network compression"):
  with members:
    - "from hashing to cnns training binaryweight networks via hashing"
    - "towards a theoretical understanding of hashing based neural nets"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "compressing neural networks with the hashing trick"
    - "predicting parameters in deep learning"
    - "do deep nets really need to be deep"

Connected through hyperedge HE-3 (induced by source paper "sparsely grouped input variables for neural networks"):
  with members:
    - "pyramidal recurrent unit for language modeling"
    - "self normalizing neural networks"
    - "dynamic routing between capsules"
    - "learning structured sparsity in deep neural networks"
    - "learning the number of neurons in deep networks"

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)