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: "from softmax to sparsemax a sparse model of attention and multi label classification"
  Abstract: We propose sparsemax, a new activation function similar to the traditional softmax, but able to output sparse probabilities. After deriving its properties, we show how its Jacobian can be efficiently computed, enabling its use in a network trained with backpropagation. Then, we propose a new smooth and convex loss function which is the sparsemax analogue of the logistic loss. We reveal an unexpected connection between this new loss and the Huber classification loss. We obtain promising empirical results in multi-label classification problems and in attention-based neural networks for natural language inference. For the latter, we achieve a similar performance as the traditional softmax, but with a selective, more compact, attention focus.

Connected through hyperedge HE-1 (induced by source paper "power bert accelerating bert inference for classification tasks"):
  with members:
    - "are sixteen heads really better than one"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "adaptively sparse transformers"
    - "q bert hessian based ultra low precision quantization of bert"
    - "sparse sequence to sequence models"
    - "explicit sparse transformer concentrated attention through explicit selection"
    - "albert a lite bert for self supervised learning of language representations"

Connected through hyperedge HE-2 (induced by source paper "lp sparsemap differentiable relaxed optimization for sparse structured prediction"):
  with members:
    - "learning latent trees with stochastic perturbations and differentiable dynamic programming"
    - "sparse sequence to sequence models"
    - "differentiable convex optimization layers"
    - "meta learning with implicit gradients"
    - "listops a diagnostic dataset for latent tree learning"
    - "backpropagating through structured argmax using a spigot"
    - "differentiable perturb and parse semi supervised parsing with a structured variational autoencoder"
    - "structured attention networks"

Connected through hyperedge HE-3 (induced by source paper "scaling up multiagent reinforcement learning for robotic systems learn an adaptive sparse communication graph"):
  with members:
    - "adaptively sparse transformers"
    - "learning transferable cooperative behavior in multi agent teams"
    - "graph policy gradients for large scale robot control"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "tarmac targeted multi agent communication"
    - "actor attention critic for multi agent reinforcement learning"
    - "mean field multi agent reinforcement learning"
    - "graph convolutional reinforcement learning for multi agent cooperation"

Connected through hyperedge HE-4 (induced by source paper "sparse sinkhorn attention"):
  with members:
    - "the evolved transformer"
    - "transformer xl attentive language models beyond a fixed length context"
    - "star transformer"
    - "generating long sequences with sparse transformers"
    - "simple and effective curriculum pointer generator networks for reading comprehension over long narratives"
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "mesh tensorflow deep learning for supercomputers"

Connected through hyperedge HE-5 (induced by source paper "multi dimensional explanation of ratings from reviews"):
  with members:
    - "is attention interpretable"
    - "attention is not explanation"
    - "methods for interpreting and understanding deep neural networks"
    - "deep contextualized word representations"
    - "deriving machine attention from human rationales"
    - "attention is all you need"
    - "rationalizing neural predictions"
    - "the concrete distribution a continuous relaxation of discrete random variables"

Connected through hyperedge HE-6 (induced by source paper "rp dnn a tweet level propagation context based deep neural networks for early rumor detection in social media"):
  with members:
    - "attention based neural architecture for rumor detection with author context awareness"
    - "neural language model based training data augmentation for weakly supervised early rumor detection"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "detection and resolution of rumours in social media a survey"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "attention is all you need"
    - "csi a hybrid deep model for fake news detection"

Connected through hyperedge HE-7 (induced by source paper "context aware non linear and neural attentive knowledge based models for grade prediction"):
  with members:
    - "ale additive latent effect models for grade prediction"
    - "nais neural attentive item similarity model for recommendation"
    - "on controllable sparse alternatives to softmax"
    - "neural factorization machines for sparse predictive analytics"
    - "attention is all you need"
    - "grade prediction with temporal course wise influence"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"
    - "a decomposable attention model for natural language inference"

Connected through hyperedge HE-8 (induced by source paper "gsanet semantic segmentation with global and selective attention"):
  with members:
    - "acfnet attentional class feature network for semantic segmentation"
    - "asymmetric non local neural networks for semantic segmentation"
    - "ocnet object context network for scene parsing"
    - "ccnet criss cross attention for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "dual attention network for scene segmentation"
    - "context encoding for semantic segmentation"
    - "attention is all you need"

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.CL(Computation and Language)