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: "limits of end to end learning"
  Abstract: End-to-end learning refers to training a possibly complex learning system by applying gradient-based learning to the system as a whole. End-to-end learning system is specifically designed so that all modules are differentiable. In effect, not only a central learning machine, but also all "peripheral" modules like representation learning and memory formation are covered by a holistic learning process. The power of end-to-end learning has been demonstrated on many tasks, like playing a whole array of Atari video games with a single architecture. While pushing for solutions to more challenging tasks, network architectures keep growing more and more complex. #R##N#In this paper we ask the question whether and to what extent end-to-end learning is a future-proof technique in the sense of scaling to complex and diverse data processing architectures. We point out potential inefficiencies, and we argue in particular that end-to-end learning does not make optimal use of the modular design of present neural networks. Our surprisingly simple experiments demonstrate these inefficiencies, up to the complete breakdown of learning.

Connected through hyperedge HE-1 (induced by source paper "a deep reinforcement learning based driving policy for autonomous road vehicles"):
  with members:
    - "elements of effective deep reinforcement learning towards tactical driving decision making"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "on a formal model of safe and scalable self driving cars"
    - "towards full automated drive in urban environments a demonstration in gomentum station california"
    - "brain inspired cognitive model with attention for self driving cars"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "end to end learning of driving models from large scale video datasets"

Connected through hyperedge HE-2 (induced by source paper "multipurpose intelligent process automation via conversational assistant"):
  with members:
    - "global to local memory pointer networks for task oriented dialogue"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a survey on dialogue systems recent advances and new frontiers"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "rasa open source language understanding and dialogue management"
    - "a network based end to end trainable task oriented dialogue system"

Connected through hyperedge HE-3 (induced by source paper "dropout prediction over weeks in moocs by learning representations of clicks and videos"):
  with members:
    - "gritnet student performance prediction with deep learning"
    - "delving deeper into mooc student dropout prediction"
    - "likely to stop predicting stopout in massive open online courses"
    - "your click decides your fate leveraging clickstream patterns in mooc videos to infer students information processing and attrition behavior"
    - "capturing attrition intensifying structural traits from didactic interaction sequences of mooc learners"

Connected through hyperedge HE-4 (induced by source paper "on incorporating semantic prior knowlegde in deep learning through embedding space constraints"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "actively seeking and learning from live data"
    - "hyperbolic neural networks"
    - "compositional attention networks for machine reasoning"
    - "deep learning a critical appraisal"
    - "visual question answering as a meta learning task"
    - "don t just assume look and answer overcoming priors for visual question answering"

Connected through hyperedge HE-5 (induced by source paper "topological navigation graph"):
  with members:
    - "an algorithmic perspective on imitation learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "semi parametric topological memory for navigation"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "learning to drive from simulation without real world labels"
    - "deep object centric policies for autonomous driving"
    - "on offline evaluation of vision based driving models"
    - "learning deployable navigation policies at kilometer scale from a single traversal"

Connected through hyperedge HE-6 (induced by source paper "darwinml a graph based evolutionary algorithm for automated machine learning"):
  with members:
    - "autostacker a compositional evolutionary learning system"
    - "progressive neural architecture search"
    - "feature engineering for predictive modeling using reinforcement learning"
    - "large scale evolution of image classifiers"
    - "pmlb a large benchmark suite for machine learning evaluation and comparison"
    - "evaluation of a tree based pipeline optimization tool for automating data science"
    - "automating biomedical data science through tree based pipeline optimization"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-7 (induced by source paper "potential field interpretable and unified representation for trajectory prediction"):
  with members:
    - "human motion trajectory prediction a survey"
    - "looking to relations for future trajectory forecast"
    - "what the constant velocity model can teach us about pedestrian motion prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "mx lstm mixing tracklets and vislets to jointly forecast trajectories and head poses"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "augmenting learning components for safety in resource constrained autonomous robots"):
  with members:
    - "reachable set computation and safety verification for neural networks with relu activations"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "deeppicar a low cost deep neural network based autonomous car"
    - "deeptest automated testing of deep neural network driven autonomous cars"
    - "a component based simplex architecture for high assurance cyber physical systems"
    - "end to end learning for self driving cars"
    - "reinforcement learning a survey"

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)