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: "improving negative sampling for word representation using self embedded features"
  Abstract: Although the word-popularity based negative sampler has shown superb performance in the skip-gram model, the theoretical motivation behind oversampling popular (non-observed) words as negative samples is still not well understood. In this paper, we start from an investigation of the gradient vanishing issue in the skip-gram model without a proper negative sampler. By performing an insightful analysis from the stochastic gradient descent (SGD) learning perspective, we demonstrate that, both theoretically and intuitively, negative samples with larger inner product scores are more informative than those with lower scores for the SGD learner in terms of both convergence rate and accuracy. Understanding this, we propose an alternative sampling algorithm that dynamically selects informative negative samples during each SGD update. More importantly, the proposed sampler accounts for multi-dimensional self-embedded features during the sampling process, which essentially makes it more effective than the original popularity-based (one-dimensional) sampler. Empirical experiments further verify our observations, and show that our fine-grained samplers gain significant improvement over the existing ones without increasing computational complexity.

Connected through hyperedge HE-1 (induced by source paper "characterizing the impact of geometric properties of word embeddings on task performance"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "factors influencing the surprising instability of word embeddings"
    - "second order word embeddings from nearest neighbor topological features"
    - "insights into analogy completion from the biomedical domain"
    - "the role of context types and dimensionality in learning word embeddings"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-2 (induced by source paper "enhancing out of domain utterance detection with data augmentation based on word embeddings"):
  with members:
    - "contextual out of domain utterance handling with counterfeit data augmentation"
    - "joint learning of domain classification and out of domain detection with dynamic class weighting for satisficing false acceptance rates"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-3 (induced by source paper "learning determinantal point processes by corrective negative sampling"):
  with members:
    - "adversarial contrastive estimation"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
    - "fast dpp sampling for nystr om with application to kernel methods"
    - "training region based object detectors with online hard example mining"
    - "kronecker determinantal point processes"
    - "fixed point algorithms for learning determinantal point processes"
    - "distributed representations of words and phrases and their compositionality"
    - "a fast and simple algorithm for training neural probabilistic language models"

Connected through hyperedge HE-4 (induced by source paper "adversarial training of word2vec for basket completion"):
  with members:
    - "deep sets"
    - "approximation and convergence properties of generative adversarial learning"
    - "maximum likelihood augmented discrete generative adversarial networks"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "swivel improving embeddings by noticing what s missing"
    - "improved techniques for training gans"
    - "convolutional neural networks for sentence classification"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "exploring student check in behavior for improved point of interest prediction"):
  with members:
    - "task guided and path augmented heterogeneous network embedding for author identification"
    - "geo sage a geographical sparse additive generative model for spatial item recommendation"
    - "pte predictive text embedding through large scale heterogeneous text networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "learning determinantal point processes by sampling inferred negatives"):
  with members:
    - "training region based object detectors with online hard example mining"
    - "fixed point algorithms for learning determinantal point processes"
    - "distributed representations of words and phrases and their compositionality"
    - "a fast and simple algorithm for training neural probabilistic language models"
    - "learning mixtures of submodular shells with application to document summarization"

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)