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: "learning compressed transforms with low displacement rank"
  Abstract: The low displacement rank (LDR) framework for structured matrices represents a matrix through two displacement operators and a low-rank residual. Existing use of LDR matrices in deep learning has applied fixed displacement operators encoding forms of shift invariance akin to convolutions. We introduce a class of LDR matrices with more general displacement operators, and explicitly learn over both the operators and the low-rank component. This class generalizes several previous constructions while preserving compression and efficient computation. We prove bounds on the VC dimension of multi-layer neural networks with structured weight matrices and show empirically that our compact parameterization can reduce the sample complexity of learning. When replacing weight layers in fully-connected, convolutional, and recurrent neural networks for image classification and language modeling tasks, our new classes exceed the accuracy of existing compression approaches, and on some tasks also outperform general unstructured layers while using more than 20x fewer parameters.

Connected through hyperedge HE-1 (induced by source paper "low memory neural network training a technical report"):
  with members:
    - "traditional and heavy tailed self regularization in neural network models"
    - "pay less attention with lightweight and dynamic convolutions"
    - "learning fast algorithms for linear transforms using butterfly factorizations"
    - "trained quantization thresholds for accurate and efficient fixed point inference of deep neural networks"
    - "memory efficient adaptive optimization"
    - "sparse evolutionary deep learning with over one million artificial neurons on commodity hardware"
    - "fixup initialization residual learning without normalization"
    - "full deep neural network training on a pruned weight budget"

Connected through hyperedge HE-2 (induced by source paper "learning fast algorithms for linear transforms using butterfly factorizations"):
  with members:
    - "neural arithmetic logic units"
    - "quadrature based features for kernel approximation"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "strassennets deep learning with a multiplication budget"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "neural combinatorial optimization with reinforcement learning"
    - "orthogonal random features"

Connected through hyperedge HE-3 (induced by source paper "run time efficient rnn compression for inference on edge devices"):
  with members:
    - "compressing rnns for iot devices by 15 38x using kronecker products"
    - "measuring scheduling efficiency of rnns for nlp applications"
    - "efficient recurrent neural networks using structured matrices in fpgas"
    - "structured weight matrices based hardware accelerators in deep neural networks fpgas and asics"
    - "alternating multi bit quantization for recurrent neural networks"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "block sparse recurrent neural networks"
    - "breaking the softmax bottleneck a high rank rnn language model"

Connected through hyperedge HE-4 (induced by source paper "equivariant transformer networks"):
  with members:
    - "on the convergence of adam and beyond"
    - "sparse unsupervised capsules generalize better"
    - "learning steerable filters for rotation equivariant cnns"
    - "polar transformer networks"
    - "rotation equivariant vector field networks"
    - "oriented response networks"
    - "dynamic routing between capsules"
    - "dynamic steerable blocks in deep residual networks"

Connected through hyperedge HE-5 (induced by source paper "understanding and training deep diagonal circulant neural networks"):
  with members:
    - "implicit regularization in deep matrix factorization"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "training compact deep learning models for video classification using circulant matrices"
    - "deep complex networks"
    - "learnable pooling with context gating for video classification"
    - "youtube 8m a large scale video classification benchmark"
    - "acdc a structured efficient linear layer"
    - "compressing neural networks with the hashing trick"

Connected through hyperedge HE-6 (induced by source paper "pushing the limits of rnn compression"):
  with members:
    - "ternary hybrid neural tree networks for highly constrained iot applications"
    - "run time efficient rnn compression for inference on edge devices"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "measuring scheduling efficiency of rnns for nlp applications"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "hello edge keyword spotting on microcontrollers"
    - "factorization tricks for lstm networks"
    - "deep convolutional and recurrent models for human activity recognition using wearables"

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)