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: "building high level features using large scale unsupervised learning"
  Abstract: We consider the problem of building high- level, class-specific feature detectors from only unlabeled data. For example, is it possible to learn a face detector using only unlabeled images? To answer this, we train a 9-layered locally connected sparse autoencoder with pooling and local contrast normalization on a large dataset of images (the model has 1 bil- lion connections, the dataset has 10 million 200x200 pixel images downloaded from the Internet). We train this network using model parallelism and asynchronous SGD on a clus- ter with 1,000 machines (16,000 cores) for three days. Contrary to what appears to be a widely-held intuition, our experimental re- sults reveal that it is possible to train a face detector without having to label images as containing a face or not. Control experiments show that this feature detector is robust not only to translation but also to scaling and out-of-plane rotation. We also find that the same network is sensitive to other high-level concepts such as cat faces and human bod- ies. Starting with these learned features, we trained our network to obtain 15.8% accu- racy in recognizing 20,000 object categories from ImageNet, a leap of 70% relative im- provement over the previous state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "certified robustness to label flipping attacks via randomized smoothing"):
  with members:
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "on defending against label flipping attacks on malware detection systems"
    - "certified adversarial robustness via randomized smoothing"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "compressed sensing with adversarial sparse noise via l1 regression"
    - "learning with bad training data via iterative trimmed loss minimization"
    - "efficient algorithms and lower bounds for robust linear regression"
    - "representer point selection for explaining deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "music generation by deep learning challenges and directions"):
  with members:
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "music style transfer issues a position paper"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "the machine learning algorithm as creative musical tool"
    - "music style transfer a position paper"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "a functional taxonomy of music generation systems"

Connected through hyperedge HE-3 (induced by source paper "biologically motivated deep learning method using hierarchical competitive learning"):
  with members:
    - "unsupervised learning by competing hidden units"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "competitive learning enriches learning representation and accelerates the fine tuning of cnns"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "how auto encoders could provide credit assignment in deep networks via target propagation"
    - "going deeper with convolutions"
    - "difference target propagation"
    - "winner take all autoencoders"

Connected through hyperedge HE-4 (induced by source paper "local aggregation for unsupervised learning of visual embeddings"):
  with members:
    - "contrastive multiview coding"
    - "data efficient image recognition with contrastive predictive coding"
    - "learning representations by maximizing mutual information across views"
    - "deep clustering for unsupervised learning of visual features"
    - "unsupervised representation learning by predicting image rotations"
    - "improving generalization via scalable neighborhood component analysis"
    - "representation learning with contrastive predictive coding"
    - "self supervised feature learning by learning to spot artifacts"

Connected through hyperedge HE-5 (induced by source paper "a survey on distributed machine learning"):
  with members:
    - "mxnet mpi embedding mpi parallelism in parameter server task model for scaling deep learning"
    - "how to backdoor federated learning"
    - "no peek a survey of private distributed deep learning"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"
    - "scaling deep learning on gpu and knights landing clusters"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-6 (induced by source paper "learning to predict without looking ahead world models without forward prediction"):
  with members:
    - "weight agnostic neural networks"
    - "reinforcement learning for improving agent design"
    - "model based reinforcement learning for atari"
    - "videoflow a flow based generative model for video"
    - "deep neuroevolution of recurrent and discrete world models"
    - "learning latent dynamics for planning from pixels"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "lipschitz continuity in model based reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "unsupervised deep tracking"):
  with members:
    - "real time mdnet"
    - "long term tracking in the wild a benchmark"
    - "a twofold siamese network for real time object tracking"
    - "end to end active object tracking and its real world deployment via reinforcement learning"
    - "deep attentive tracking via reciprocative learning"
    - "vital visual tracking via adversarial learning"
    - "learning dynamic memory networks for object tracking"
    - "trackingnet a large scale dataset and benchmark for object tracking in the wild"

Connected through hyperedge HE-8 (induced by source paper "accelerated training for cnn distributed deep learning through automatic resource aware layer placement"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "beyond data and model parallelism for deep neural networks"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "parameter hub a rack scale parameter server for distributed deep neural network training"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "pipedream fast and efficient pipeline parallel dnn training"
    - "exploring hidden dimensions in parallelizing convolutional neural networks"
    - "gradient sparsification for communication efficient distributed optimization"

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)