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: "propto svm for learning with label proportions"
  Abstract: We study the problem of learning with label proportions in which the training data is provided in groups and only the proportion of each class in each group is known. We propose a new method called proportion-SVM, or $\propto$SVM, which explicitly models the latent unknown instance labels together with the known group label proportions in a large-margin framework. Unlike the existing works, our approach avoids making restrictive assumptions about the data. The $\propto$SVM model leads to a non-convex integer programming problem. In order to solve it efficiently, we propose two algorithms: one based on simple alternating optimization and the other based on a convex relaxation. Extensive experiments on standard datasets show that $\propto$SVM outperforms the state-of-the-art, especially for larger group sizes.

Connected through hyperedge HE-1 (induced by source paper "discriminative video representation learning using support vector classifiers"):
  with members:
    - "moments in time dataset one million videos for event understanding"
    - "a short note about kinetics 600"
    - "non linear temporal subspace representations for activity recognition"
    - "video representation learning using discriminative pooling"
    - "higher order pooling of cnn features via kernel linearization for action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "generalized rank pooling for activity recognition"
    - "appearance and relation networks for video classification"

Connected through hyperedge HE-2 (induced by source paper "learning from label proportions with consistency regularization"):
  with members:
    - "deep multi class learning from label proportions"
    - "mixmatch a holistic approach to semi supervised learning"
    - "deep learning from label proportions for emphysema quantification"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "co training for demographic classification using deep learning from label proportions"
    - "temporal ensembling for semi supervised learning"
    - "wide residual networks"

Connected through hyperedge HE-3 (induced by source paper "transfer learning based label proportions method with data of uncertainty"):
  with members:
    - "mining the demographics of political sentiment from twitter using learning from label proportions"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-4 (induced by source paper "video representation learning using discriminative pooling"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "generalized rank pooling for activity recognition"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "ordered pooling of optical flow sequences for action recognition"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"

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)