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: "efficient exact gradient update for training deep networks with very large sparse targets"
  Abstract: An important class of problems involves training deep neural networks with sparse prediction targets of very high dimension D. These occur naturally in e.g. neural language models or the learning of word-embeddings, often posed as predicting the probability of next words among a vocabulary of size D (e.g. 200 000). Computing the equally large, but typically non-sparse D-dimensional output vector from a last hidden layer of reasonable dimension d (e.g. 500) incurs a prohibitive O(Dd) computational cost for each example, as does updating the D x d output weight matrix and computing the gradient needed for backpropagation to previous layers. While efficient handling of large sparse network inputs is trivial, the case of large sparse targets is not, and has thus so far been sidestepped with approximate alternatives such as hierarchical softmax or sampling-based approximations during training. In this work we develop an original algorithmic approach which, for a family of loss functions that includes squared error and spherical softmax, can compute the exact loss, gradient update for the output weights, and gradient for backpropagation, all in O(d^2) per example instead of O(Dd), remarkably without ever computing the D-dimensional output. The proposed algorithm yields a speedup of D/4d , i.e. two orders of magnitude for typical sizes, for that critical part of the computations that often dominates the training time in this kind of network architecture.

Connected through hyperedge HE-1 (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-2 (induced by source paper "ans adaptive network scaling for deep rectifier reinforcement learning models"):
  with members:
    - "on characterizing the capacity of neural networks using algebraic topology"
    - "deep reinforcement learning that matters"
    - "proximal policy optimization algorithms"
    - "asynchronous methods for deep reinforcement learning"
    - "learning values across many orders of magnitude"
    - "q prop sample efficient policy gradient with an off policy critic"
    - "benchmarking deep reinforcement learning for continuous control"
    - "deep reinforcement learning in large discrete action spaces"

Connected through hyperedge HE-3 (induced by source paper "on controllable sparse alternatives to softmax"):
  with members:
    - "opennmt open source toolkit for neural machine translation"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "end to end memory networks"
    - "a neural attention model for abstractive sentence summarization"
    - "describing multimedia content using attention based encoder decoder networks"

Connected through hyperedge HE-4 (induced by source paper "neural machine translation and sequence to sequence models a tutorial"):
  with members:
    - "lstm a search space odyssey"
    - "online segment to segment neural transduction"
    - "sequence to sequence learning as beam search optimization"
    - "memory enhanced decoder for neural machine translation"
    - "learning to translate in real time with neural machine translation"
    - "toward multilingual neural machine translation with universal encoder and decoder"
    - "recurrent neural network grammars"
    - "coverage embedding models for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "deep reinforcement learning that matters"):
  with members:
    - "parameter space noise for exploration"
    - "moba a new arena for game ai"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "reproducibility of benchmarked deep reinforcement learning tasks for continuous control"
    - "starcraft ii a new challenge for reinforcement learning"
    - "third person imitation learning"
    - "towards generalization and simplicity in continuous control"
    - "interpolated policy gradient merging on policy and off policy gradient estimation for deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "getting deep recommenders fit bloom embeddings for sparse binary input output networks"):
  with members:
    - "information theoretical label embeddings for large scale image classification"
    - "wide deep learning for recommender systems"
    - "efficient softmax approximation for gpus"
    - "label embedding for image classification"
    - "hybrid recommender system based on autoencoders"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "inferring networks of substitutable and complementary products"

Connected through hyperedge HE-7 (induced by source paper "improving negative sampling for word representation using self embedded features"):
  with members:
    - "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "matrix factorization using window sampling and negative sampling for improved word representations"
    - "dependency recurrent neural language models for sentence completion"
    - "neural programmer inducing latent programs with gradient descent"
    - "wordrank learning word embeddings via robust ranking"
    - "character aware neural language models"
    - "a deep architecture for semantic matching with multiple positional sentence representations"

Connected through hyperedge HE-8 (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"

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)