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: "generating long term trajectories using deep hierarchical networks"
  Abstract: We study the problem of modeling spatiotemporal trajectories over long time horizons using expert demonstrations. For instance, in sports, agents often choose action sequences with long-term goals in mind, such as achieving a certain strategic position. Conventional policy learning approaches, such as those based on Markov decision processes, generally fail at learning cohesive long-term behavior in such high-dimensional state spaces, and are only effective when myopic modeling lead to the desired behavior. The key difficulty is that conventional approaches are "shallow" models that only learn a single state-action policy. We instead propose a hierarchical policy class that automatically reasons about both long-term and short-term goals, which we instantiate as a hierarchical neural network. We showcase our approach in a case study on learning to imitate demonstrated basketball trajectories, and show that it generates significantly more realistic trajectories compared to non-hierarchical baselines as judged by professional sports analysts.

Connected through hyperedge HE-1 (induced by source paper "multiresolution tensor learning for efficient and interpretable spatial analysis"):
  with members:
    - "near optimal sketching of low rank tensor regression"
    - "multiscale analysis for higher order tensors"
    - "tensor regression meets gaussian processes"
    - "multiresolution recurrent neural networks an application to dialogue response generation"
    - "tensorly tensor learning in python"
    - "why should i trust you explaining the predictions of any classifier"
    - "parallel tensor compression for large scale scientific data"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "deep learning for spatio temporal data mining a survey"):
  with members:
    - "t gcn a temporal graph convolutional network for traffic prediction"
    - "learning from multiple cities a meta learning approach for spatial temporal prediction"
    - "a comprehensive survey on graph neural networks"
    - "inferring transportation modes from gps trajectories using a convolutional neural network"
    - "where to go next a spatio temporal lstm model for next poi recommendation"
    - "forecasting transportation network speed using deep capsule networks with nested lstm models"
    - "a hybrid method for traffic flow forecasting using multimodal deep learning"
    - "deep sequence learning with auxiliary information for traffic prediction"

Connected through hyperedge HE-3 (induced by source paper "multi agent reinforcement learning with multi step generative models"):
  with members:
    - "learning latent dynamics for planning from pixels"
    - "model ensemble trust region policy optimization"
    - "deep imitative models for flexible inference planning and control"
    - "generative modeling of multimodal multi human behavior"
    - "unity a general platform for intelligent agents"
    - "learning latent subspaces in variational autoencoders"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "prediction and control with temporal segment models"

Connected through hyperedge HE-4 (induced by source paper "naomi non autoregressive multiresolution sequence imputation"):
  with members:
    - "gain missing data imputation using generative adversarial nets"
    - "brits bidirectional recurrent imputation for time series"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "generating multi agent trajectories using programmatic weak supervision"
    - "end to end non autoregressive neural machine translation with connectionist temporal classification"
    - "progressive growing of gans for improved quality stability and variation"
    - "parallel wavenet fast high fidelity speech synthesis"

Connected through hyperedge HE-5 (induced by source paper "associative embedding for game agnostic team discrimination"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "guided upsampling network for real time semantic segmentation"
    - "contextnet exploring context and detail for semantic segmentation in real time"
    - "tracking emerges by colorizing videos"
    - "real time semantic image segmentation via spatial sparsity"
    - "icnet for real time semantic segmentation on high resolution images"
    - "enet a deep neural network architecture for real time semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "improved structural discovery and representation learning of multi agent data"):
  with members:
    - "google research football a novel reinforcement learning environment"
    - "exploiting temporal context for 3d human pose estimation in the wild"
    - "learning 3d human dynamics from video"
    - "generative multi agent behavioral cloning"
    - "exploiting temporal information for 3d human pose estimation"
    - "deeppermnet visual permutation learning"
    - "fine grained retrieval of sports plays using tree based alignment of trajectories"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-7 (induced by source paper "lego leveraging experience in roadmap generation for sampling based planning"):
  with members:
    - "using local experiences for global motion planning"
    - "robot motion planning in learned latent spaces"
    - "deep sequential models for sampling based planning"
    - "deeply informed neural sampling for robot motion planning"
    - "learning implicit sampling distributions for motion planning"
    - "approximate inference based motion planning by learning and exploiting low dimensional latent variable models"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "learning sampling distributions for robot motion planning"

Connected through hyperedge HE-8 (induced by source paper "empowering a search algorithms with neural networks for personalized route recommendation"):
  with members:
    - "understanding urban dynamics via context aware tensor factorization with neighboring regularization"
    - "cd cnn a partially supervised cross domain deep learning model or urban resident recognition"
    - "learning points and routes to recommend trajectories"
    - "gated feedback recurrent neural networks"
    - "neural machine translation by jointly learning to align and translate"

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)