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: "discrete continuous admm for transductive inference in higher order mrfs"
  Abstract: This paper introduces a novel algorithm for transductive inference in higher-order MRFs, where the unary energies are parameterized by a variable classifier. The considered task is posed as a joint optimization problem in the continuous classifier parameters and the discrete label variables. In contrast to prior approaches such as convex relaxations, we propose an advantageous decoupling of the objective function into discrete and continuous subproblems and a novel, efficient optimization method related to ADMM. This approach preserves integrality of the discrete label variables and guarantees global convergence to a critical point. We demonstrate the advantages of our approach in several experiments including video object segmentation on the DAVIS data set and interactive image segmentation.

Connected through hyperedge HE-1 (induced by source paper "discretely constrained deep network for weakly supervised segmentation"):
  with members:
    - "prior aware neural network for partially supervised multi organ segmentation"
    - "curriculum semi supervised segmentation"
    - "constrained cnn losses for weakly supervised segmentation"
    - "beyond gradient descent for regularized segmentation losses"
    - "predicting deeper into the future of semantic segmentation"
    - "unbiased shape compactness for segmentation"
    - "dope distributed optimization for pairwise energies"
    - "enet a deep neural network architecture for real time semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "projection convolutional neural networks for 1 bit cnns via discrete back propagation"):
  with members:
    - "training and inference with integers in deep neural networks"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "training wide residual networks for deployment using a single bit for each weight"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "oriented response networks"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-3 (induced by source paper "adversarial data programming using gans to relax the bottleneck of curated labeled data"):
  with members:
    - "toward controlled generation of text"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "triangle generative adversarial networks"
    - "comparison of maximum likelihood and gan based training of real nvps"
    - "triple generative adversarial nets"
    - "inferring generative model structure with static analysis"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "beyond gradient descent for regularized segmentation losses"):
  with members:
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "efficient graph cut optimization for full crfs with quantized edges"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "unbiased shape compactness for segmentation"
    - "dope distributed optimization for pairwise energies"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "training neural networks without gradients a scalable admm approach"

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)