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: "blending lstms into cnns"
  Abstract: We consider whether deep convolutional networks (CNNs) can represent decision functions with similar accuracy as recurrent networks such as LSTMs. First, we show that a deep CNN with an architecture inspired by the models recently introduced in image recognition can yield better accuracy than previous convolutional and LSTM networks on the standard 309h Switchboard automatic speech recognition task. Then we show that even more accurate CNNs can be trained under the guidance of LSTMs using a variant of model compression, which we call model blending because the teacher and student models are similar in complexity but different in inductive bias. Blending further improves the accuracy of our CNN, yielding a computationally efficient model of accuracy higher than any of the other individual models. Examining the effect of "dark knowledge" in this model compression task, we find that less than 1% of the highest probability labels are needed for accurate model compression.

Connected through hyperedge HE-1 (induced by source paper "cnns lstms and attention networks for pathology detection in medical data"):
  with members:
    - "attention u net learning where to look for the pancreas"
    - "attention gated networks for improving ultrasound scan plane detection"
    - "learn to pay attention"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "two phase learning for weakly supervised object localization"
    - "comparative study of cnn and rnn for natural language processing"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "convolutional recurrent neural networks for electrocardiogram classification"

Connected through hyperedge HE-2 (induced by source paper "feed feature level ensemble for knowledge distillation"):
  with members:
    - "paraphrasing complex network network compression via factor transfer"
    - "knowledge distillation by on the fly native ensemble"
    - "amalgamating knowledge towards comprehensive classification"
    - "efficient neural architecture search via parameter sharing"
    - "deep mutual learning"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "data distillation towards omni supervised learning"
    - "like what you like knowledge distill via neuron selectivity transfer"

Connected through hyperedge HE-3 (induced by source paper "learning approximate inference networks for structured prediction"):
  with members:
    - "calibrating energy based generative adversarial networks"
    - "a continuous relaxation of beam search for end to end training of neural sequence models"
    - "sequence level knowledge distillation"
    - "perceptual losses for real time style transfer and super resolution"
    - "improved techniques for training gans"
    - "distilling an ensemble of greedy dependency parsers into one mst parser"
    - "input convex neural networks"
    - "structured prediction energy networks"

Connected through hyperedge HE-4 (induced by source paper "sequence level knowledge distillation"):
  with members:
    - "character based neural machine translation"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "deep recurrent models with fast forward connections for neural machine translation"
    - "learning compact recurrent neural networks"
    - "distilling an ensemble of greedy dependency parsers into one mst parser"
    - "exploring the limits of language modeling"
    - "auto sizing neural networks with applications to n gram language models"
    - "multilingual language processing from bytes"

Connected through hyperedge HE-5 (induced by source paper "active long term memory networks"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "graying the black box understanding dqns"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "building machines that learn and think like people"
    - "bridging the gaps between residual learning recurrent neural networks and visual cortex"
    - "a roadmap towards machine intelligence"
    - "actor mimic deep multitask and transfer reinforcement 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)