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: "adaptive sampled softmax with kernel based sampling"
  Abstract: Softmax is the most commonly used output function for multiclass problems and is widely used in areas such as vision, natural language processing, and recommendation. A softmax model has linear costs in the number of classes which makes it too expensive for many real-world problems. A common approach to speed up training involves sampling only some of the classes at each training step. It is known that this method is biased and that the bias increases the more the sampling distribution deviates from the output distribution. Nevertheless, almost any recent work uses simple sampling distributions that require a large sample size to mitigate the bias. In this work, we propose a new class of kernel based sampling methods and develop an efficient sampling algorithm. Kernel based sampling adapts to the model as it is trained, thus resulting in low bias. Kernel based sampling can be easily applied to many models because it relies only on the model's last hidden layer. We empirically study the trade-off of bias, sampling distribution and sample size and show that kernel based sampling results in low bias with few samples.

Connected through hyperedge HE-1 (induced by source paper "pre training tasks for embedding based large scale retrieval"):
  with members:
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "latent retrieval for weakly supervised open domain question answering"
    - "reqa an evaluation for end to end answer retrieval models"
    - "distilling task specific knowledge from bert into simple neural networks"
    - "poly encoders transformer architectures and pre training strategies for fast and accurate multi sentence scoring"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "stochastic negative mining for learning with large output spaces"

Connected through hyperedge HE-2 (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"
    - "efficient softmax approximation for gpus"
    - "ds mlr exploiting double separability for scaling up distributed multinomial logistic regression"
    - "orthogonal random features"

Connected through hyperedge HE-3 (induced by source paper "slide in defense of smart algorithms over hardware acceleration for large scale deep learning systems"):
  with members:
    - "lsh sampling breaks the computation chicken and egg loop in adaptive stochastic gradient estimation"
    - "data curation with deep learning vision towards self driving data curation"
    - "auto tuning tensorflow threading model for cpu backend"
    - "scaling up split merge mcmc with locality sensitive sampling lss"
    - "in datacenter performance analysis of a tensor processing unit"
    - "flash randomized algorithms accelerated over cpu gpu for ultra high dimensional similarity search"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "database meets deep learning challenges and opportunities"

Connected through hyperedge HE-4 (induced by source paper "context aware deep model for entity recommendation in search engine at alibaba"):
  with members:
    - "long tail relation extraction via knowledge graph embeddings and graph convolution networks"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "attention based capsule networks with dynamic routing for relation extraction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fasttext zip compressing text classification models"
    - "node2vec scalable feature learning for networks"
    - "querying knowledge graphs by example entity tuples"
    - "distributed representations of words and phrases and their compositionality"

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)