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: "stochastic negative mining for learning with large output spaces"
  Abstract: We consider the problem of retrieving the most relevant labels for a given input when the size of the output space is very large. Retrieval methods are modeled as set-valued classifiers which output a small set of classes for each input, and a mistake is made if the label is not in the output set. Despite its practical importance, a statistically principled, yet practical solution to this problem is largely missing. To this end, we first define a family of surrogate losses and show that they are calibrated and convex under certain conditions on the loss parameters and data distribution, thereby establishing a statistical and analytical basis for using these losses. Furthermore, we identify a particularly intuitive class of loss functions in the aforementioned family and show that they are amenable to practical implementation in the large output space setting (i.e. computation is possible without evaluating scores of all labels) by developing a technique called Stochastic Negative Mining. We also provide generalization error bounds for the losses in the family. Finally, we conduct experiments which demonstrate that Stochastic Negative Mining yields benefits over commonly used negative sampling approaches.

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"
    - "training millions of personalized dialogue agents"

Connected through hyperedge HE-2 (induced by source paper "sampled softmax with random fourier features"):
  with members:
    - "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"
    - "orthogonal random features"

Connected through hyperedge HE-3 (induced by source paper "x bert extreme multi label text classification with using bidirectional encoder representations from transformers"):
  with members:
    - "bonsai diverse and shallow trees for extreme multi label classification"
    - "haxmlnet hierarchical attention network for extreme multi label text classification"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "attentionxml extreme multi label text classification with multi label attention based recurrent neural networks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "instance based transfer learning for multilingual deep retrieval"):
  with members:
    - "what comes next extractive summarization by next sentence prediction"
    - "gromov wasserstein alignment of word embedding spaces"
    - "distant supervision from disparate sources for low resource part of speech tagging"
    - "unsupervised multilingual word embeddings"
    - "know what you don t know unanswerable questions for squad"
    - "fine tuned language models for text classification"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)