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: "fast amortized inference and learning in log linear models with randomly perturbed nearest neighbor search"
  Abstract: Inference in log-linear models scales linearly in the size of output space in the worst-case. This is often a bottleneck in natural language processing and computer vision tasks when the output space is feasibly enumerable but very large. We propose a method to perform inference in log-linear models with sublinear amortized cost. Our idea hinges on using Gumbel random variable perturbations and a pre-computed Maximum Inner Product Search data structure to access the most-likely elements in sublinear amortized time. Our method yields provable runtime and accuracy guarantees. Further, we present empirical experiments on ImageNet and Word Embeddings showing significant speedups for sampling, inference, and learning in log-linear models.

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"
    - "adaptive sampled softmax with kernel based sampling"
    - "efficient softmax approximation for gpus"
    - "ds mlr exploiting double separability for scaling up distributed multinomial logistic regression"
    - "orthogonal random features"

Connected through hyperedge HE-2 (induced by source paper "doubly sparse sparse mixture of sparse experts for efficient softmax inference"):
  with members:
    - "learning to screen for fast softmax inference on large vocabulary neural networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learning k way d dimensional discrete code for compact embedding representations"
    - "efficient softmax approximation for gpus"
    - "pointer sentinel mixture models"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-3 (induced by source paper "multi resolution hashing for fast pairwise summations"):
  with members:
    - "hardness of approximate nearest neighbor search"
    - "data dependent coresets for compressing neural networks with applications to generalization bounds"
    - "hashing based estimators for kernel density in high dimensions"
    - "on the fine grained complexity of empirical risk minimization kernel methods and neural networks"
    - "stochastic optimization with bandit sampling"
    - "tight cell probe bounds for succinct boolean matrix vector multiplication"
    - "distance sensitive hashing"
    - "optimal hashing based time space trade offs for approximate near neighbors"

Connected through hyperedge HE-4 (induced by source paper "approximate inference via weighted rademacher complexity"):
  with members:
    - "high dimensional inference with random maximum a posteriori perturbations"
    - "provable submodular minimization using wolfe s algorithm"
    - "a scalable approximate model counter"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "optimization with parity constraints from binary codes to discrete integration"
    - "on sampling from the gibbs distribution with random maximum a posteriori perturbations"
    - "on the partition function and random maximum a posteriori perturbations"
    - "learning with submodular functions a convex optimization perspective"

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)