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: "convolutional residual memory networks"
  Abstract: Very deep convolutional neural networks (CNNs) yield state of the art results on a wide variety of visual recognition problems. A number of state of the the art methods for image recognition are based on networks with well over 100 layers and the performance vs. depth trend is moving towards networks in excess of 1000 layers. In such extremely deep architectures the vanishing or exploding gradient problem becomes a key issue. Recent evidence also indicates that convolutional networks could benefit from an interface to explicitly constructed memory mechanisms interacting with a CNN feature processing hierarchy. Correspondingly, we propose and evaluate a memory mechanism enhanced convolutional neural network architecture based on augmenting convolutional residual networks with a long short term memory mechanism. We refer to this as a convolutional residual memory network. To the best of our knowledge this approach can yield state of the art performance on the CIFAR-100 benchmark and compares well with other state of the art techniques on the CIFAR-10 and SVHN benchmarks. This is achieved using networks with more breadth, much less depth and much less overall computation relative to comparable deep ResNets without the memory mechanism. Our experiments and analysis explore the importance of the memory mechanism, network depth, breadth, and predictive performance.

Connected through hyperedge HE-1 (induced by source paper "recent advances in deep learning an overview"):
  with members:
    - "deep learning a critical appraisal"
    - "adversarial attacks on neural network policies"
    - "deep learning for environmentally robust speech recognition an overview of recent developments"
    - "lstm a search space odyssey"
    - "a knowledge grounded neural conversation model"
    - "me r cnn multi expert region based cnn for object detection"
    - "on the origin of deep learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-2 (induced by source paper "residual networks of residual networks multilevel residual networks"):
  with members:
    - "weighted residuals for very deep networks"
    - "resnet in resnet generalizing residual architectures"
    - "deep residual networks with exponential linear unit"
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "wide residual networks"
    - "fractalnet ultra deep neural networks without residuals"
    - "swapout learning an ensemble of deep architectures"

Connected through hyperedge HE-3 (induced by source paper "deep convolutional neural network design patterns"):
  with members:
    - "residual networks of residual networks multilevel residual networks"
    - "weighted residuals for very deep networks"
    - "multi residual networks improving the speed and accuracy of residual networks"
    - "convolutional neural fabrics"
    - "resnet in resnet generalizing residual architectures"
    - "deep networks with stochastic depth"
    - "driving in the matrix can virtual worlds replace human generated annotations for real world tasks"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-4 (induced by source paper "batch normalized recurrent highway networks"):
  with members:
    - "lstm a search space odyssey"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "deep networks with stochastic depth"
    - "recurrent highway networks"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "recurrent batch normalization"
    - "deep residual learning for image recognition"

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.CV(Computer Vision and Pattern Recognition)