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: "on the partition function and random maximum a posteriori perturbations"
  Abstract: In this paper we relate the partition function to the max-statistics of random variables. In particular, we provide a novel framework for approximating and bounding the partition function using MAP inference on randomly perturbed models. As a result, we can use efficient MAP solvers such as graph-cuts to evaluate the corresponding partition function. We show that our method excels in the typical "high signal - high coupling" regime that results in ragged energy landscapes difficult for alternative approaches.

Connected through hyperedge HE-1 (induced by source paper "learning with differentiable perturbed optimizers"):
  with members:
    - "differentiable convex optimization layers"
    - "differentiation of blackbox combinatorial solvers"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "importance weighting without importance weights an efficient algorithm for combinatorial semi bandits"
    - "online linear optimization via smoothing"
    - "on measure concentration of random maximum a posteriori perturbations"
    - "on sampling from the gibbs distribution with random maximum a posteriori perturbations"
    - "optimization with sparsity inducing penalties"

Connected through hyperedge HE-2 (induced by source paper "towards efficient discrete integration via adaptive quantile queries"):
  with members:
    - "approximate knowledge compilation by online collapsed importance sampling"
    - "probabilistic model counting with short xors"
    - "stable model counting and its application in probabilistic logic programming"
    - "distribution aware sampling and weighted model counting for sat"
    - "loopy belief propagation for approximate inference an empirical study"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"

Connected through hyperedge HE-3 (induced by source paper "liteeval a coarse to fine framework for resource efficient video recognition"):
  with members:
    - "scsampler sampling salient clips from video for efficient action recognition"
    - "eco efficient convolutional network for online video understanding"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "autofocus efficient multi scale inference"
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "adaframe adaptive frame selection for fast video recognition"
    - "sniper efficient multi scale training"
    - "slowfast networks for video recognition"

Connected through hyperedge HE-4 (induced by source paper "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "understanding back translation at scale"
    - "neural machine translation with gumbel greedy decoding"
    - "attention is all you need"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "classical structured prediction losses for sequence to sequence learning"
    - "convolutional sequence to sequence learning"
    - "a continuous relaxation of beam search for end to end training of neural sequence models"

Connected through hyperedge HE-5 (induced by source paper "joint learning of geometric and probabilistic constellation shaping"):
  with members:
    - "deep learning for channel coding via neural mutual information estimation"
    - "geometric constellation shaping for fiber optic communication systems via end to end learning"
    - "model free training of end to end communication systems"
    - "an introduction to deep learning for the physical layer"
    - "on probabilistic shaping of quadrature amplitude modulation for the nonlinear fiber channel"
    - "constant composition distribution matching"
    - "adam a method for stochastic optimization"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"

Connected through hyperedge HE-6 (induced by source paper "counterfactual off policy evaluation with gumbel max structural causal models"):
  with members:
    - "representation balancing mdps for off policy policy evaluation"
    - "entropic causal inference"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "data efficient off policy policy evaluation for reinforcement learning"
    - "distinguishing cause from effect using observational data methods and benchmarks"
    - "counterfactual probabilities computational methods bounds and applications"
    - "probabilities of causation bounds and identification"

Connected through hyperedge HE-7 (induced by source paper "adawish faster discrete integration via adaptive quantiles"):
  with members:
    - "approximate knowledge compilation by online collapsed importance sampling"
    - "probabilistic model counting with short xors"
    - "closing the gap between short and long xors for model counting"
    - "stable model counting and its application in probabilistic logic programming"
    - "distribution aware sampling and weighted model counting for sat"
    - "loopy belief propagation for approximate inference an empirical study"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"

Connected through hyperedge HE-8 (induced by source paper "inducing and embedding senses with scaled gumbel softmax"):
  with members:
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "deep contextualized word representations"
    - "wic 10 000 example pairs for evaluating context sensitive representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "probabilistic fasttext for multi sense word embeddings"
    - "a mixture model for learning multi sense word embeddings"
    - "muse modularizing unsupervised sense embeddings"

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)