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: "semi supervised deep learning by metric embedding"
  Abstract: Deep networks are successfully used as classification models yielding state-of-the-art results when trained on a large number of labeled samples. These models, however, are usually much less suited for semi-supervised problems because of their tendency to overfit easily when trained on small amounts of data. In this work we will explore a new training objective that is targeting a semi-supervised regime with only a small subset of labeled data. This criterion is based on a deep metric embedding over distance relations within the set of labeled samples, together with constraints over the embeddings of the unlabeled set. The final learned representations are discriminative in euclidean space, and hence can be used with subsequent nearest-neighbor classification using the labeled samples.

Connected through hyperedge HE-1 (induced by source paper "end to end graph based deep semi supervised learning"):
  with members:
    - "randaugment practical automated data augmentation with a reduced search space"
    - "remixmatch semi supervised learning with distribution alignment and augmentation anchoring"
    - "label propagation for deep semi supervised learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised learning via compact latent space clustering"
    - "realistic evaluation of deep semi supervised learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "training object detectors from few weakly labeled and many unlabeled images"):
  with members:
    - "weakly supervised object detection with segmentation collaboration"
    - "object aware instance labeling for weakly supervised object detection"
    - "few shot object detection with attention rpn and multi relation detector"
    - "few example object detection with model communication"
    - "feature weighting and boosting for few shot segmentation"
    - "label propagation for deep semi supervised learning"
    - "adaptive masked proxies for few shot segmentation"
    - "mixmatch a holistic approach to semi supervised learning"

Connected through hyperedge HE-3 (induced by source paper "autonomous aerial cinematography in unstructured environments with learned artistic decision making"):
  with members:
    - "improved generalization of heading direction estimation for aerial filming using semi supervised regression"
    - "can a robot become a movie director learning artistic principles for aerial cinematography"
    - "towards a robust aerial cinematography platform localizing and tracking moving targets in unstructured environments"
    - "autonomous drone cinematographer using artistic principles to create smooth safe occlusion free trajectories for aerial filming"
    - "integrating kinematics and environment context into deep inverse reinforcement learning for predicting off road vehicle trajectories"
    - "optimizing for aesthetically pleasing quadrotor camera motion"
    - "good semi supervised learning that requires a bad gan"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-4 (induced by source paper "improved generalization of heading direction estimation for aerial filming using semi supervised regression"):
  with members:
    - "autonomous drone cinematographer using artistic principles to create smooth safe occlusion free trajectories for aerial filming"
    - "integrating kinematics and environment context into deep inverse reinforcement learning for predicting off road vehicle trajectories"
    - "deep directional statistics pose estimation with uncertainty quantification"
    - "good semi supervised learning that requires a bad gan"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "performance measures and a data set for multi target multi camera tracking"
    - "human body orientation estimation using convolutional neural network"
    - "realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-5 (induced by source paper "label prediction framework for semi supervised cross modal retrieval"):
  with members:
    - "iterative learning with open set noisy labels"
    - "sch gan semi supervised cross modal hashing by generative adversarial network"
    - "learning from noisy large scale datasets with minimal supervision"
    - "temporal ensembling for semi supervised learning"
    - "semi supervised learning with ladder networks"

Connected through hyperedge HE-6 (induced by source paper "semi supervised deep learning using improved unsupervised discriminant projection"):
  with members:
    - "good semi supervised learning that requires a bad gan"
    - "revisiting semi supervised learning with graph embeddings"
    - "semi supervised learning with ladder networks"
    - "semi supervised learning with deep generative models"

Connected through hyperedge HE-7 (induced by source paper "a review of semi supervised learning theories and recent advances"):
  with members:
    - "deep generative image models using a laplacian pyramid of adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "ensemble manifold segmentation for model distillation and semi supervised learning"):
  with members:
    - "improved techniques for training gans"
    - "learning convolutional neural networks for graphs"
    - "unsupervised visual representation learning by context prediction"
    - "unsupervised learning of visual representations using videos"
    - "deep residual learning for image recognition"
    - "cross modal distillation for supervision transfer"
    - "very deep convolutional networks for large scale image recognition"
    - "discriminative unsupervised feature learning with exemplar convolutional neural networks"

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)