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: "learning by transduction"
  Abstract: We describe a method for predicting a classification of an object given classifications of the objects in the training set, assuming that the pairs object/classification are generated by an i.i.d. process from a continuous probability distribution. Our method is a modification of Vapnik's support-vector machine; its main novelty is that it gives not only the prediction itself but also a practicable measure of the evidence found in support of that prediction. We also describe a procedure for assigning degrees of confidence to predictions made by the support vector machine. Some experimental results are presented, and possible extensions of the algorithms are discussed.

Connected through hyperedge HE-1 (induced by source paper "relational neural machines"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "neural logic machines"
    - "lyrics a general interface layer to integrate ai and deep learning"
    - "neural logic reinforcement learning"
    - "neural markov logic networks"
    - "learning relational representations with auto encoding logic programs"
    - "deepproblog neural probabilistic logic programming"
    - "on a convex logic fragment for learning and reasoning"

Connected through hyperedge HE-2 (induced by source paper "semi supervised learning via conditional rotation angle estimation"):
  with members:
    - "mixmatch a holistic approach to semi supervised learning"
    - "revisiting self supervised visual representation learning"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "unsupervised representation learning by predicting image rotations"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "mixup beyond empirical risk minimization"
    - "undeepvo monocular visual odometry through unsupervised deep learning"

Connected through hyperedge HE-3 (induced by source paper "a hierarchical transitive aligned graph kernel for un attributed graphs"):
  with members:
    - "a quantum inspired similarity measure for the analysis of complete weighted graphs"
    - "learning convolutional neural networks for graphs"
    - "diffusion convolutional neural networks"
    - "subgraph matching kernels for attributed graphs"
    - "graph kernels between point clouds"

Connected through hyperedge HE-4 (induced by source paper "on the equivalence between node embeddings and structural graph representations"):
  with members:
    - "from community to role based graph embeddings"
    - "graph representation ensemble learning"
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "on the limitations of representing functions on sets"
    - "position aware graph neural networks"
    - "what can neural networks reason about"
    - "correlated variational auto encoders"
    - "on the equivalence between graph isomorphism testing and function approximation with gnns"

Connected through hyperedge HE-5 (induced by source paper "mixmatch a holistic approach to semi supervised learning"):
  with members:
    - "deep metric transfer for label propagation with limited annotated data"
    - "autoaugment learning augmentation policies from data"
    - "three mechanisms of weight decay regularization"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "invariant information clustering for unsupervised image classification and segmentation"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "decoupled weight decay regularization"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-6 (induced by source paper "negative sampling in semi supervised learning"):
  with members:
    - "mixmatch a holistic approach to semi supervised learning"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "mixup beyond empirical risk minimization"
    - "improved techniques for training gans"
    - "densely connected convolutional networks"
    - "wide residual networks"
    - "regularization with stochastic transformations and perturbations for deep semi supervised learning"

Connected through hyperedge HE-7 (induced by source paper "remixmatch semi supervised learning with distribution alignment and augmentation anchoring"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "unsupervised data augmentation for consistency training"
    - "mixmatch a holistic approach to semi supervised learning"
    - "deep metric transfer for label propagation with limited annotated data"
    - "autoaugment learning augmentation policies from data"
    - "unsupervised representation learning by predicting image rotations"
    - "three mechanisms of weight decay regularization"
    - "realistic evaluation of deep semi supervised learning algorithms"

Connected through hyperedge HE-8 (induced by source paper "learning aligned spatial graph convolutional networks for graph classification"):
  with members:
    - "concept drift and anomaly detection in graph streams"
    - "anonymous walk embeddings"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "generalizing the convolution operator to extend cnns to irregular domains"
    - "deep convolutional networks on graph structured data"

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)