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: "sim real joint reinforcement transfer for 3d indoor navigation"
  Abstract: There has been an increasing interest in 3D indoor navigation, where a robot in an environment moves to a target according to an instruction. To deploy a robot for navigation in the physical world, lots of training data is required to learn an effective policy. It is quite labour intensive to obtain sufficient real environment data for training robots while synthetic data is much easier to construct by rendering. Though it is promising to utilize the synthetic environments to facilitate navigation training in the real world, real environment are heterogeneous from synthetic environment in two aspects. First, the visual representation of the two environments have significant variances. Second, the houseplans of these two environments are quite different. Therefore two types of information, i.e. visual representation and policy behavior, need to be adapted in the reinforcement model. The learning procedure of visual representation and that of policy behavior are presumably reciprocal. We propose to jointly adapt visual representation and policy behavior to leverage the mutual impacts of environment and policy. Specifically, our method employs an adversarial feature adaptation model for visual representation transfer and a policy mimic strategy for policy behavior imitation. Experiment shows that our method outperforms the baseline by 19.47% without any additional human annotations.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning based visual navigation with information theoretic regularization"):
  with members:
    - "learning a controller fusion network by online trajectory filtering for vision based uav racing"
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "bayesian relational memory for semantic visual navigation"
    - "reinforcement learning from imperfect demonstrations"
    - "deep drone racing learning agile flight in dynamic environments"
    - "a real time game theoretic planner for autonomous two player drone racing"
    - "oil observational imitation learning"
    - "cirl controllable imitative reinforcement learning for vision based self driving"

Connected through hyperedge HE-2 (induced by source paper "manga method agnostic neural policy generalization and adaptation"):
  with members:
    - "learning to augment synthetic images for sim2real policy transfer"
    - "sim to real transfer for biped locomotion"
    - "norml no reward meta learning"
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "exploration by random network distillation"
    - "the lyapunov neural network adaptive stability certification for safe learning of dynamical systems"
    - "learning dexterous in hand manipulation"
    - "policy transfer with strategy optimization"

Connected through hyperedge HE-3 (induced by source paper "dm gan dynamic memory generative adversarial networks for text to image synthesis"):
  with members:
    - "adversarial learning with local coordinate coding"
    - "spectral normalization for generative adversarial networks"
    - "text to image synthesis via symmetrical distillation networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "semantic image synthesis via adversarial learning"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "adversarial discriminative domain adaptation"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "functionality oriented convolutional filter pruning"):
  with members:
    - "revisiting the importance of individual units in cnns via ablation"
    - "scsp spectral clustering filter pruning with soft self adaption manners"
    - "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "rethinking the value of network pruning"
    - "nisp pruning networks using neuron importance score propagation"
    - "channel pruning for accelerating very deep neural networks"
    - "learning structured sparsity in deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation"):
  with members:
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "significance aware information bottleneck for domain adaptive semantic segmentation"
    - "learning to adapt structured output space for semantic segmentation"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "semantic aware grad gan for virtual to real urban scene adaption"
    - "deep adversarial attention alignment for unsupervised domain adaptation the benefit of target expectation maximization"
    - "macro micro adversarial network for human parsing"
    - "a fully convolutional tri branch network fctn for domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "filter pruning via geometric median for deep convolutional neural networks acceleration"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "searching for a robust neural architecture in four gpu hours"
    - "exploring linear relationship in feature map subspace for convnets compression"
    - "learning to prune filters in convolutional neural networks"
    - "paraphrasing complex network network compression via factor transfer"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "discrimination aware channel pruning for deep neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"

Connected through hyperedge HE-7 (induced by source paper "interpretable convolutional filter pruning"):
  with members:
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "revisiting the importance of individual units in cnns via ablation"
    - "scsp spectral clustering filter pruning with soft self adaption manners"
    - "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "rethinking the value of network pruning"
    - "how convolutional neural network see the world a survey of convolutional neural network visualization methods"

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.CV(Computer Vision and Pattern Recognition)