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: "cold start reinforcement learning with softmax policy gradients"
  Abstract: Policy-gradient approaches to reinforcement learning have two common and undesirable overhead procedures, namely warm-start training and sample variance reduction. In this paper, we describe a reinforcement learning method based on a softmax value function that requires neither of these procedures. Our method combines the advantages of policy-gradient methods with the efficiency and simplicity of maximum-likelihood approaches. We apply this new cold-start reinforcement learning method in training sequence generation models for structured output prediction problems. Empirical evidence validates this method on automatic summarization and image captioning tasks.

Connected through hyperedge HE-1 (induced by source paper "generalization in generation a closer look at exposure bias"):
  with members:
    - "deep reinforcement learning with distributional semantic rewards for abstractive summarization"
    - "quantifying exposure bias for neural language generation"
    - "sample efficient text summarization using a single pre trained transformer"
    - "autoregressive text generation beyond feedback loops"
    - "deep reinforcement learning for sequence to sequence models"
    - "neural text generation past present and beyond"
    - "learning actionable representations with goal conditioned policies"
    - "isolating sources of disentanglement in variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "reinforcing an image caption generator using off line human feedback"):
  with members:
    - "vifidel evaluating the visual fidelity of image descriptions"
    - "neural naturalist generating fine grained image comparisons"
    - "informative image captioning with external sources of information"
    - "graph rise graph regularized image semantic embedding"
    - "decoupled box proposal and featurization with ultrafine grained semantic labels improve image captioning and visual question answering"
    - "neural baby talk"
    - "reward learning from human preferences and demonstrations in atari"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"

Connected through hyperedge HE-3 (induced by source paper "optimal completion distillation for sequence learning"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "token level and sequence level loss smoothing for rnn language models"
    - "switchout an efficient data augmentation algorithm for neural machine translation"
    - "convergence of value aggregation for imitation learning"
    - "softmax q distribution estimation for structured prediction a theoretical interpretation for raml"
    - "improved training of end to end attention models for speech recognition"

Connected through hyperedge HE-4 (induced by source paper "connecting the dots between mle and rl for sequence generation"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "unsupervised text style transfer using language models as discriminators"
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "policy shaping and generalized update equations for semantic parsing from denotations"
    - "softmax q distribution estimation for structured prediction a theoretical interpretation for raml"
    - "toward controlled generation of text"
    - "selective encoding for abstractive sentence summarization"
    - "equivalence between policy gradients and soft q learning"

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)