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: "an exploration of softmax alternatives belonging to the spherical loss family"
  Abstract: In a multi-class classification problem, it is standard to model the output of a neural network as a categorical distribution conditioned on the inputs. The output must therefore be positive and sum to one, which is traditionally enforced by a softmax. This probabilistic mapping allows to use the maximum likelihood principle, which leads to the well-known log-softmax loss. However the choice of the softmax function seems somehow arbitrary as there are many other possible normalizing functions. It is thus unclear why the log-softmax loss would perform better than other loss alternatives. In particular Vincent et al. (2015) recently introduced a class of loss functions, called the spherical family, for which there exists an efficient algorithm to compute the updates of the output weights irrespective of the output size. In this paper, we explore several loss functions from this family as possible alternatives to the traditional log-softmax. In particular, we focus our investigation on spherical bounds of the log-softmax loss and on two spherical log-likelihood losses, namely the log-Spherical Softmax suggested by Vincent et al. (2015) and the log-Taylor Softmax that we introduce. Although these alternatives do not yield as good results as the log-softmax loss on two language modeling tasks, they surprisingly outperform it in our experiments on MNIST and CIFAR-10, suggesting that they might be relevant in a broad range of applications.

Connected through hyperedge HE-1 (induced by source paper "hyperspectral classification based on 3d asymmetric inception network with data fusion transfer learning"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "secure evaluation of quantized neural networks"):
  with members:
    - "cryptflow secure tensorflow inference"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "xonn xnor based oblivious deep neural network inference"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "training and inference with integers in deep neural networks"
    - "a survey on methods and theories of quantized neural networks"
    - "training deep neural networks with 8 bit floating point numbers"
    - "chameleon a hybrid secure computation framework for machine learning applications"

Connected through hyperedge HE-3 (induced by source paper "robust deep neural networks inspired by fuzzy logic"):
  with members:
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "adversarial examples are a natural consequence of test error in noise"
    - "a kernelized manifold mapping to diminish the effect of adversarial perturbations"
    - "understanding adversarial robustness the trade off between minimum and average margin"
    - "adversarial training versus weight decay"
    - "sparse dnns with improved adversarial robustness"
    - "neural networks should be wide enough to learn disconnected decision regions"
    - "adversarially robust generalization requires more data"

Connected through hyperedge HE-4 (induced by source paper "sampled softmax with random fourier features"):
  with members:
    - "stochastic negative mining for learning with large output spaces"
    - "normface l 2 hypersphere embedding for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "fast amortized inference and learning in log linear models with randomly perturbed nearest neighbor search"
    - "adaptive sampled softmax with kernel based sampling"
    - "efficient softmax approximation for gpus"
    - "ds mlr exploiting double separability for scaling up distributed multinomial logistic regression"

Connected through hyperedge HE-5 (induced by source paper "von mises fisher loss for training sequence to sequence models with continuous outputs"):
  with members:
    - "billion scale similarity search with gpus"
    - "mimicking word embeddings using subword rnns"
    - "stronger baselines for trustable results in neural machine translation"
    - "get to the point summarization with pointer generator networks"
    - "neural machine translation via binary code prediction"
    - "enriching word vectors with subword information"
    - "using the output embedding to improve language models"
    - "a neural attention model for abstractive sentence summarization"

Connected through hyperedge HE-6 (induced by source paper "learning invariant riemannian geometric representations using deep nets"):
  with members:
    - "end to end saliency mapping via probability distribution prediction"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "geometric deep learning going beyond euclidean data"
    - "se3 nets learning rigid body motion using deep neural networks"
    - "building deep networks on grassmann manifolds"
    - "generalized backpropagation etude de cas orthogonality"
    - "deep learning on lie groups for skeleton based action recognition"
    - "learning convolutional neural networks for graphs"

Connected through hyperedge HE-7 (induced by source paper "adaptive sampled softmax with kernel based sampling"):
  with members:
    - "clustering is efficient for approximate maximum inner product search"
    - "speeding up neural networks for large scale classification using wta hashing"
    - "strategies for training large vocabulary neural language models"
    - "efficient exact gradient update for training deep networks with very large sparse targets"
    - "recurrent neural network regularization"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "from softmax to sparsemax a sparse model of attention and multi label classification"):
  with members:
    - "end to end memory networks"
    - "a neural attention model for abstractive sentence summarization"
    - "learning to transduce with unbounded memory"
    - "reasoning about entailment with neural attention"
    - "a large annotated corpus for learning natural language inference"
    - "attention based models for speech recognition"
    - "teaching machines to read and comprehend"
    - "adam a method for stochastic optimization"

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.NE(Neural and Evolutionary Computing)