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: "softtarget regularization an effective technique to reduce over fitting in neural networks"
  Abstract: Deep neural networks are learning models with a very high capacity and therefore prone to over-fitting. Many regularization techniques such as Dropout, DropConnect, and weight decay all attempt to solve the problem of over-fitting by reducing the capacity of their respective models (Srivastava et al., 2014), (Wan et al., 2013), (Krogh & Hertz, 1992). In this paper we introduce a new form of regularization that guides the learning problem in a way that reduces over-fitting without sacrificing the capacity of the model. The mistakes that models make in early stages of training carry information about the learning problem. By adjusting the labels of the current epoch of training through a weighted average of the real labels, and an exponential average of the past soft-targets we achieved a regularization scheme as powerful as Dropout without necessarily reducing the capacity of the model, and simplified the complexity of the learning problem. SoftTarget regularization proved to be an effective tool in various neural network architectures.

Connected through hyperedge HE-1 (induced by source paper "deepmimic mentor student unlabeled data based training"):
  with members:
    - "stealing knowledge from protected deep neural networks using composite unlabeled data"
    - "copycat cnn stealing knowledge by persuading confession with random non labeled data"
    - "knowledge distillation in generations more tolerant teachers educate better students"
    - "robust student network learning"
    - "learning transferable architectures for scalable image recognition"
    - "low shot learning with imprinted weights"
    - "mixup beyond empirical risk minimization"
    - "squeeze and excitation networks"

Connected through hyperedge HE-2 (induced by source paper "regularizing output distribution of abstractive chinese social media text summarization for improved semantic consistency"):
  with members:
    - "a hierarchical end to end model for jointly improving text summarization and sentiment classification"
    - "word embedding attention network generating words by querying distributed word representations for paraphrase generation"
    - "decoding history based adaptive control of attention for neural machine translation"
    - "dp gan diversity promoting generative adversarial network for generating informative and diversified text"
    - "get to the point summarization with pointer generator networks"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "supervised attentions for neural machine translation"
    - "a neural attention model for abstractive sentence summarization"

Connected through hyperedge HE-3 (induced by source paper "a practical approach to insertion with variable socket position using deep reinforcement learning"):
  with members:
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "a framework for robot manipulation skill formalism meta learning and adaptive control"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "setting up a reinforcement learning task with a real world robot"
    - "deep reinforcement learning for high precision assembly tasks"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "a distributional perspective on reinforcement learning"
    - "overcoming exploration in reinforcement learning with demonstrations"

Connected through hyperedge HE-4 (induced by source paper "supervised domain enablement attention for personalized domain classification"):
  with members:
    - "self normalizing neural networks"
    - "structured attention networks"
    - "lstm a search space odyssey"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "harnessing deep neural networks with logic rules"
    - "neural machine translation with supervised attention"
    - "temporal ensembling for semi supervised learning"

Connected through hyperedge HE-5 (induced by source paper "towards better decoding and language model integration in sequence to sequence models"):
  with members:
    - "very deep convolutional networks for end to end speech recognition"
    - "sequence to sequence learning as beam search optimization"
    - "disturblabel regularizing cnn on the loss layer"
    - "learning online alignments with continuous rewards policy gradient"
    - "globally normalized transition based neural networks"
    - "asynchronous methods for deep reinforcement learning"
    - "lip reading sentences in the wild"
    - "a coverage embedding model for neural machine translation"

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)