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: "noise contrastive estimation and negative sampling for conditional models consistency and statistical efficiency"
  Abstract: Noise Contrastive Estimation (NCE) is a powerful parameter estimation method for log-linear models, which avoids calculation of the partition function or its derivatives at each training step, a computationally demanding step in many cases. It is closely related to negative sampling methods, now widely used in NLP. This paper considers NCE-based estimation of conditional models. Conditional models are frequently encountered in practice; however there has not been a rigorous theoretical analysis of NCE in this setting, and we will argue there are subtle but important questions when generalizing NCE to the conditional case. In particular, we analyze two variants of NCE for conditional models: one based on a classification objective, the other based on a ranking objective. We show that the ranking-based variant of NCE gives consistent parameter estimates under weaker assumptions than the classification-based method; we analyze the statistical efficiency of the ranking-based and classification-based variants of NCE; finally we describe experiments on synthetic data and language modeling showing the effectiveness and trade-offs of both methods.

Connected through hyperedge HE-1 (induced by source paper "convergence of end to end training in deep unsupervised contrasitive learning"):
  with members:
    - "a simple framework for contrastive learning of visual representations"
    - "how much over parameterization is sufficient to learn deep relu networks"
    - "contrastive multiview coding"
    - "an improved analysis of training over parameterized deep neural networks"
    - "generalization error bounds of gradient descent for learning over parameterized deep relu networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "momentum contrast for unsupervised visual representation learning"
    - "polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks"

Connected through hyperedge HE-2 (induced by source paper "predictive coding for locally linear control"):
  with members:
    - "predictive coding for boosting deep reinforcement learning with sparse rewards"
    - "unsupervised state representation learning in atari"
    - "model based reinforcement learning for atari"
    - "on variational bounds of mutual information"
    - "prediction consistency curvature representation learning for locally linear control"
    - "learning latent dynamics for planning from pixels"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "learning plannable representations with causal infogan"

Connected through hyperedge HE-3 (induced by source paper "unsupervised state representation learning in atari"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "on variational bounds of mutual information"
    - "multi object representation learning with iterative variational inference"
    - "wasserstein dependency measure for representation learning"
    - "learning representations by maximizing mutual information across views"
    - "revisiting self supervised visual representation learning"
    - "object oriented dynamics predictor"
    - "near optimal representation learning for hierarchical reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "learning representations by maximizing mutual information across views"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "contrastive multiview coding"
    - "data efficient image recognition with contrastive predictive coding"
    - "on variational bounds of mutual information"
    - "mixmatch a holistic approach to semi supervised learning"
    - "revisiting self supervised visual representation learning"
    - "rethinking imagenet pre training"
    - "learning actionable representations from visual observations"

Connected through hyperedge HE-5 (induced by source paper "on mutual information maximization for representation learning"):
  with members:
    - "a theory of usable information under computational constraints"
    - "learning video representations using contrastive bidirectional transformer"
    - "contrastive multiview coding"
    - "data efficient image recognition with contrastive predictive coding"
    - "on variational bounds of mutual information"
    - "wasserstein dependency measure for representation learning"
    - "learning representations by maximizing mutual information across views"
    - "rethinking lossy compression the rate distortion perception tradeoff"

Connected through hyperedge HE-6 (induced by source paper "energy inspired models learning with sampler induced distributions"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "divide and couple using monte carlo variational objectives for posterior approximation"
    - "importance weighting and variational inference"
    - "kernel exponential family estimation via doubly dual embedding"
    - "hamiltonian variational auto encoder"
    - "learning descriptor networks for 3d shape synthesis and analysis"
    - "stochastic video generation with a learned prior"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-7 (induced by source paper "on variational bounds of mutual information"):
  with members:
    - "entropy and mutual information in models of deep neural networks"
    - "invariant representations without adversarial training"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "formal limitations on the measurement of mutual information"
    - "isolating sources of disentanglement in variational autoencoders"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"
    - "representation learning with contrastive predictive coding"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"

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.CL(Computation and Language)