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: "neural arithmetic logic units"
  Abstract: Neural networks can learn to represent and manipulate numerical information, but they seldom generalize well outside of the range of numerical values encountered during training. To encourage more systematic numerical extrapolation, we propose an architecture that represents numerical quantities as linear activations which are manipulated using primitive arithmetic operators, controlled by learned gates. We call this module a neural arithmetic logic unit (NALU), by analogy to the arithmetic logic unit in traditional processors. Experiments show that NALU-enhanced neural networks can learn to track time, perform arithmetic over images of numbers, translate numerical language into real-valued scalars, execute computer code, and count objects in images. In contrast to conventional architectures, we obtain substantially better generalization both inside and outside of the range of numerical values encountered during training, often extrapolating orders of magnitude beyond trained numerical ranges.

Connected through hyperedge HE-1 (induced by source paper "stealing black box functionality using the deep neural tree architecture"):
  with members:
    - "neural network gradient based learning of black box function interfaces"
    - "a survey on explainable artificial intelligence xai towards medical xai"
    - "knockoff nets stealing functionality of black box models"
    - "hyperbolic neural networks"
    - "query efficient black box attack by active learning"
    - "adaptive neural trees"
    - "stealing hyperparameters in machine learning"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-2 (induced by source paper "neural arithmetic units"):
  with members:
    - "learning dexterous in hand manipulation"
    - "neural arithmetic expression calculator"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "on evaluating the generalization of lstm models in formal languages"
    - "improving the neural gpu architecture for algorithm learning"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "grid long short term memory"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "inalu improved neural arithmetic logic unit"):
  with members:
    - "flow based network traffic generation using generative adversarial networks"
    - "measuring arithmetic extrapolation performance"
    - "neural arithmetic expression calculator"
    - "improving the neural gpu architecture for algorithm learning"
    - "grid long short term memory"
    - "neural programmer interpreters"
    - "adam a method for stochastic optimization"
    - "learning to execute"

Connected through hyperedge HE-4 (induced by source paper "weight agnostic neural networks"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "continual lifelong learning with neural networks a review"
    - "the evolved transformer"
    - "reinforcement learning for improving agent design"
    - "evaluating the search phase of neural architecture search"
    - "measuring the intrinsic dimension of objective landscapes"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-5 (induced by source paper "learning implicitly recurrent cnns through parameter sharing"):
  with members:
    - "convolutional neural networks with alternately updated clique"
    - "regularized evolution for image classifier architecture search"
    - "snas stochastic neural architecture search"
    - "sparsely aggregated convolutional networks"
    - "efficient neural architecture search via parameter sharing"
    - "progressive neural architecture search"
    - "making neural programming architectures generalize via recursion"
    - "deep expander networks efficient deep networks from graph theory"

Connected through hyperedge HE-6 (induced by source paper "improving discrete latent representations with differentiable approximation bridges"):
  with members:
    - "decoupled greedy learning of cnns"
    - "a new benchmark and progress toward improved weakly supervised learning"
    - "deep learning using rectified linear units relu"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "training neural networks using features replay"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "self normalizing neural networks"

Connected through hyperedge HE-7 (induced by source paper "learning fast algorithms for linear transforms using butterfly factorizations"):
  with members:
    - "quadrature based features for kernel approximation"
    - "learning compressed transforms with low displacement rank"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "strassennets deep learning with a multiplication budget"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "neural combinatorial optimization with reinforcement learning"
    - "orthogonal random features"

Connected through hyperedge HE-8 (induced by source paper "learning execution through neural code fusion"):
  with members:
    - "a neural network prefetcher for arbitrary memory access patterns"
    - "neural code comprehension a learnable representation of code semantics"
    - "learning memory access patterns"
    - "path based function embedding and its application to specification mining"
    - "code vectors understanding programs through embedded abstracted symbolic traces"
    - "ithemal accurate portable and fast basic block throughput estimation using deep neural networks"
    - "neural guided deductive search for real time program synthesis from examples"
    - "automatic program synthesis of long programs with a learned garbage collector"

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.NE(Neural and Evolutionary Computing)