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: "on controllable sparse alternatives to softmax"
  Abstract: Converting an n-dimensional vector to a probability distribution over n objects is a commonly used component in many machine learning tasks like multiclass classification, multilabel classification, attention mechanisms etc. For this, several probability mapping functions have been proposed and employed in literature such as softmax, sum-normalization, spherical softmax, and sparsemax, but there is very little understanding in terms how they relate with each other. Further, none of the above formulations offer an explicit control over the degree of sparsity. To address this, we develop a unified framework that encompasses all these formulations as special cases. This framework ensures simple closed-form solutions and existence of sub-gradients suitable for learning via backpropagation. Within this framework, we propose two novel sparse formulations, sparsegen-lin and sparsehourglass, that seek to provide a control over the degree of desired sparsity. We further develop novel convex loss functions that help induce the behavior of aforementioned formulations in the multilabel classification setting, showing improved performance. We also demonstrate empirically that the proposed formulations, when used to compute attention weights, achieve better or comparable performance on standard seq2seq tasks like neural machine translation and abstractive summarization.

Connected through hyperedge HE-1 (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-2 (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"
    - "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"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "a decomposable attention model for natural language inference"

Connected through hyperedge HE-3 (induced by source paper "towards interpretable sparse graph representation learning with laplacian pooling"):
  with members:
    - "graph convolutional networks with eigenpooling"
    - "a comprehensive survey on graph neural networks"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "hierarchical graph representation learning with differentiable pooling"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graphrnn generating realistic graphs with deep auto regressive models"

Connected through hyperedge HE-4 (induced by source paper "sparse 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"
    - "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"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "next term student performance prediction a recommender systems 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)