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 the limitations of representing functions on sets"
  Abstract: Recent work on the representation of functions on sets has considered the use of summation in a latent space to enforce permutation invariance. In particular, it has been conjectured that the dimension of this latent space may remain fixed as the cardinality of the sets under consideration increases. However, we demonstrate that the analysis leading to this conjecture requires mappings which are highly discontinuous and argue that this is only of limited practical use. Motivated by this observation, we prove that an implementation of this model via continuous mappings (as provided by e.g. neural networks or Gaussian processes) actually imposes a constraint on the dimensionality of the latent space. Practical universal function representation for set inputs can only be achieved with a latent dimension at least the size of the maximum number of input elements.

Connected through hyperedge HE-1 (induced by source paper "on learning sets of symmetric elements"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "deep models for relational databases"
    - "incidence networks for geometric deep learning"
    - "permutation invariant feature restructuring for correlation aware image set based recognition"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "multiview aggregation for learning category specific shape reconstruction"
    - "on the universality of invariant networks"
    - "a simple proof of the universality of invariant equivariant graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "language as a cognitive tool to imagine goals in curiosity driven exploration"):
  with members:
    - "emergent systematic generalization in a situated agent"
    - "skew fit state covering self supervised reinforcement learning"
    - "language as an abstraction for hierarchical deep reinforcement learning"
    - "monet unsupervised scene decomposition and representation"
    - "extending machine language models toward human level language understanding"
    - "using natural language for reward shaping in reinforcement learning"
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "multi object representation learning with iterative variational inference"

Connected through hyperedge HE-3 (induced by source paper "on the equivalence between node embeddings and structural graph representations"):
  with members:
    - "from community to role based graph embeddings"
    - "graph representation ensemble learning"
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "position aware graph neural networks"
    - "what can neural networks reason about"
    - "correlated variational auto encoders"
    - "on the equivalence between graph isomorphism testing and function approximation with gnns"
    - "a comprehensive survey on graph neural networks"

Connected through hyperedge HE-4 (induced by source paper "powerset convolutional neural networks"):
  with members:
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "learning a sat solver from single bit supervision"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "deep sets"
    - "simplicial closure and higher order link prediction"
    - "janossy pooling learning deep permutation invariant functions for variable size inputs"
    - "neural message passing for quantum chemistry"
    - "robust spatial filtering with graph convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "a hierarchy of graph neural networks based on learnable local features"):
  with members:
    - "simplifying graph convolutional networks"
    - "a comprehensive survey on graph neural networks"
    - "graph neural tangent kernel fusing graph neural networks with graph kernels"
    - "graph neural networks a review of methods and applications"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "n gcn multi scale graph convolution for semi supervised node classification"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-6 (induced by source paper "dynamic input for deep reinforcement learning in autonomous driving"):
  with members:
    - "end to end race driving with deep reinforcement learning"
    - "deep sets"
    - "addressing function approximation error in actor critic methods"
    - "adaptive behavior generation for autonomous driving using deep reinforcement learning with compact semantic states"
    - "zero shot sim to real transfer with modular priors"
    - "proximal policy optimization algorithms"
    - "embed to control a locally linear latent dynamics model for control from raw images"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-7 (induced by source paper "representing unordered data using multiset automata and complex numbers"):
  with members:
    - "robust attentional aggregation of deep feature sets for multi view 3d reconstruction"
    - "algorithms and training for weighted multiset automata and regular expressions"
    - "deep sets"
    - "janossy pooling learning deep permutation invariant functions for variable size inputs"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"

Connected through hyperedge HE-8 (induced by source paper "set transformer a framework for attention based permutation invariant neural networks"):
  with members:
    - "robust attentional aggregation of deep feature sets for multi view 3d reconstruction"
    - "attentive neural processes"
    - "attention based deep multiple instance learning"
    - "conditional neural processes"
    - "deep sets"
    - "attend and interact higher order object interactions for video understanding"
    - "attention is all you need"
    - "a simple neural network module for relational reasoning"

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)