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: "path normalized optimization of recurrent neural networks with relu activations"
  Abstract: We investigate the parameter-space geometry of recurrent neural networks (RNNs), and develop an adaptation of path-SGD optimization method, attuned to this geometry, that can learn plain RNNs with ReLU activations. On several datasets that require capturing long-term dependency structure, we show that path-SGD can significantly improve trainability of ReLU RNNs compared to RNNs trained with SGD, even with various recently suggested initialization schemes.

Connected through hyperedge HE-1 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units"):
  with members:
    - "towards non saturating recurrent units for modelling long term dependencies"
    - "what is wrong with scene text recognition model comparisons dataset and model analysis"
    - "non normal recurrent neural network nnrnn learning long time dependencies while improving expressivity with transient dynamics"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "simple recurrent units for highly parallelizable recurrence"
    - "lstm a search space odyssey"
    - "a flexible approach to automated rnn architecture generation"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"

Connected through hyperedge HE-2 (induced by source paper "independently recurrent neural network indrnn building a longer and deeper rnn"):
  with members:
    - "simple recurrent units for highly parallelizable recurrence"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "lstm a search space odyssey"
    - "pose conditioned spatio temporal attention for human action recognition"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "fast slow recurrent neural networks"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "joint distance maps based action recognition with convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "deep frank wolfe for neural network optimization"):
  with members:
    - "on the convergence of adam and beyond"
    - "smooth loss functions for deep top k classification"
    - "faster convergence generalization in dnns"
    - "online learning rate adaptation with hypergradient descent"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "exploring generalization in deep learning"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "learned optimizers that scale and generalize"

Connected through hyperedge HE-4 (induced by source paper "privado practical and secure dnn inference with enclaves"):
  with members:
    - "chiron privacy preserving machine learning as a service"
    - "mlcapsule guarded offline deployment of machine learning as a service"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "integrating remote attestation with transport layer security"
    - "tapas tricks to accelerate encrypted prediction as a service"
    - "efficient deep learning on multi source private data"
    - "gazelle a low latency framework for secure neural network inference"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-5 (induced by source paper "capacity control of relu neural networks by basis path norm"):
  with members:
    - "exploring generalization in deep learning"
    - "attention is all you need"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "spectrally normalized margin bounds for neural networks"
    - "convolutional sequence to sequence learning"
    - "wide deep learning for recommender systems"
    - "densely connected convolutional networks"
    - "a generic coordinate descent framework for learning from implicit feedback"

Connected through hyperedge HE-6 (induced by source paper "privado practical and secure dnn inference"):
  with members:
    - "chiron privacy preserving machine learning as a service"
    - "mlcapsule guarded offline deployment of machine learning as a service"
    - "efficient deep learning on multi source private data"
    - "gazelle a low latency framework for secure neural network inference"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "deepsecure scalable provably secure deep learning"
    - "inferring fine grained control flow inside sgx enclaves with branch shadowing"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "exploring generalization in deep learning"):
  with members:
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "nearly tight vc dimension and pseudodimension bounds for piecewise linear neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "norm based capacity control in neural networks"
    - "train faster generalize better stability of stochastic gradient descent"
    - "data dependent path normalization in neural 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)