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 sets"
  Abstract: We study the problem of designing models for machine learning tasks defined on \emph{sets}. In contrast to traditional approach of operating on fixed dimensional vectors, we consider objective functions defined on sets that are invariant to permutations. Such problems are widespread, ranging from estimation of population statistics \cite{poczos13aistats}, to anomaly detection in piezometer data of embankment dams \cite{Jung15Exploration}, to cosmology \cite{Ntampaka16Dynamical,Ravanbakhsh16ICML1}. Our main theorem characterizes the permutation invariant functions and provides a family of functions to which any permutation invariant objective function must belong. This family of functions has a special structure which enables us to design a deep network architecture that can operate on sets and which can be deployed on a variety of scenarios including both unsupervised and supervised learning tasks. We also derive the necessary and sufficient conditions for permutation equivariance in deep models. We demonstrate the applicability of our method on population statistic estimation, point cloud classification, set expansion, and outlier detection.

Connected through hyperedge HE-1 (induced by source paper "stochastic latent residual video prediction"):
  with members:
    - "videoflow a conditional flow based model for stochastic video generation"
    - "improved conditional vrnns for video prediction"
    - "cubic lstms for video prediction"
    - "unsupervised learning of object structure and dynamics from videos"
    - "scaling autoregressive video models"
    - "video to video synthesis"
    - "hierarchical long term video prediction without supervision"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-2 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction"):
  with members:
    - "dependency aware attention control for unconstrained face recognition with image sets"
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "attention based deep multiple instance learning"
    - "deepmvs learning multi view stereopsis"
    - "attention based pyramid aggregation network for visual place recognition"
    - "deep attentional structured representation learning for visual recognition"
    - "deep imbalanced attribute classification using visual attention aggregation"
    - "psdf fusion probabilistic signed distance function for on the fly 3d data fusion and scene reconstruction"

Connected through hyperedge HE-3 (induced by source paper "a survey on the expressive power of graph neural networks"):
  with members:
    - "random features strengthen graph neural networks"
    - "generalization and representational limits of graph neural networks"
    - "universal invariant and equivariant graph neural networks"
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "exact k recommendation via maximal clique optimization"
    - "constant time graph neural networks"
    - "kgat knowledge graph attention network for recommendation"
    - "understanding attention and generalization in graph neural networks"

Connected through hyperedge HE-4 (induced by source paper "a permutation equivariant neural network architecture for auction design"):
  with members:
    - "settling the sample complexity of single parameter revenue maximization"
    - "a neural architecture for designing truthful and efficient auctions"
    - "multi agent adversarial inverse reinforcement learning"
    - "approximation schemes for a buyer with independent items via symmetries"
    - "tight revenue gaps among simple mechanisms"
    - "a duality based unified approach to bayesian mechanism design"
    - "invariant and equivariant graph networks"
    - "tight approximation ratio of anonymous pricing"

Connected through hyperedge HE-5 (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"
    - "on the limitations of representing functions on sets"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "can graph neural networks count substructures"):
  with members:
    - "composing molecules with multiple property constraints"
    - "generalization and representational limits of graph neural networks"
    - "universal invariant and equivariant graph neural networks"
    - "g2sat learning to generate sat formulas"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "on the universality of invariant networks"
    - "what graph neural networks cannot learn depth vs width"
    - "gnn explainer a tool for post hoc explanation of graph neural networks"

Connected through hyperedge HE-7 (induced by source paper "multimodal shape completion via conditional generative adversarial networks"):
  with members:
    - "morphing and sampling network for dense point cloud completion"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "pq net a generative part seq2seq network for 3d shapes"
    - "dense 3d object reconstruction from a single depth view"
    - "unpaired point cloud completion on real scans using adversarial training"
    - "visual object networks image generation with disentangled 3d representation"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "p2p net bidirectional point displacement net for shape transform"

Connected through hyperedge HE-8 (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"
    - "on the limitations of representing functions on sets"
    - "from language to goals inverse reinforcement learning for vision based instruction following"

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)