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: "deep learning as a mixed convex combinatorial optimization problem"
  Abstract: As neural networks grow deeper and wider, learning networks with hard-threshold activations is becoming increasingly important, both for network quantization, which can drastically reduce time and energy requirements, and for creating large integrated systems of deep networks, which may have non-differentiable components and must avoid vanishing and exploding gradients for effective learning. However, since gradient descent is not applicable to hard-threshold functions, it is not clear how to learn networks of them in a principled way. We address this problem by observing that setting targets for hard-threshold hidden units in order to minimize loss is a discrete optimization problem, and can be solved as such. The discrete optimization goal is to find a set of targets such that each unit, including the output, has a linearly separable problem to solve. Given these targets, the network decomposes into individual perceptrons, which can then be learned with standard convex approaches. Based on this, we develop a recursive mini-batch algorithm for learning deep hard-threshold networks that includes the popular but poorly justified straight-through estimator as a special case. Empirically, we show that our algorithm improves classification accuracy in a number of settings, including for AlexNet and ResNet-18 on ImageNet, when compared to the straight-through estimator.

Connected through hyperedge HE-1 (induced by source paper "understanding straight through estimator in training activation quantized neural nets"):
  with members:
    - "blended coarse gradient descent for full quantization of deep neural networks"
    - "deep neural nets with interpolating function as output activation"
    - "learning one convolutional layer with overlapping patches"
    - "loss aware weight quantization of deep networks"
    - "binaryrelax a relaxation approach for training deep neural networks with quantized weights"
    - "pact parameterized clipping activation for quantized neural networks"
    - "an optimal control approach to deep learning and applications to discrete weight neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "quantization networks"):
  with members:
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "loss aware weight quantization of deep networks"
    - "performance guaranteed network acceleration via high order residual quantization"
    - "ternary residual networks"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep learning with low precision by half wave gaussian quantization"
    - "towards effective low bitwidth convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "bnn improved binary network training"):
  with members:
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "a survey on methods and theories of quantized neural networks"
    - "mnasnet platform aware neural architecture search for mobile"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "wrpn wide reduced precision networks"
    - "training quantized nets a deeper understanding"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-4 (induced by source paper "recurrent binary embedding for gpu enabled exhaustive retrieval from billion scale semantic vectors"):
  with members:
    - "billion scale similarity search with gpus"
    - "deep embedding forest forest based serving with deep embedding features"
    - "hashnet deep learning to hash by continuation"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "efficient large scale approximate nearest neighbor search on the gpu"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "ternary neural networks for resource efficient ai applications"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "combinatorial attacks on binarized neural networks"):
  with members:
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "attacking binarized neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "towards evaluating the robustness of neural networks"
    - "adversarial machine learning at scale"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "how auto encoders could provide credit assignment in deep networks via target propagation"

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)