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: "long term planning by short term prediction"
  Abstract: We consider planning problems, that often arise in autonomous driving applications, in which an agent should decide on immediate actions so as to optimize a long term objective. For example, when a car tries to merge in a roundabout it should decide on an immediate acceleration/braking command, while the long term effect of the command is the success/failure of the merge. Such problems are characterized by continuous state and action spaces, and by interaction with multiple agents, whose behavior can be adversarial. We argue that dual versions of the MDP framework (that depend on the value function and the $Q$ function) are problematic for autonomous driving applications due to the non Markovian of the natural state space representation, and due to the continuous state and action spaces. We propose to tackle the planning task by decomposing the problem into two phases: First, we apply supervised learning for predicting the near future based on the present. We require that the predictor will be differentiable with respect to the representation of the present. Second, we model a full trajectory of the agent using a recurrent neural network, where unexplained factors are modeled as (additive) input nodes. This allows us to solve the long-term planning problem using supervised learning techniques and direct optimization over the recurrent neural network. Our approach enables us to learn robust policies by incorporating adversarial elements to the environment.

Connected through hyperedge HE-1 (induced by source paper "solving optimization problems through fully convolutional networks an application to the travelling salesman problem"):
  with members:
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "deep learning on graphs a survey"
    - "fixed parameter algorithms for rectilinear steiner tree and rectilinear traveling salesman problem in the plane"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "end to end learning for self driving cars"
    - "neural combinatorial optimization with reinforcement learning"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "predicting vehicle behaviors over an extended horizon using behavior interaction network"):
  with members:
    - "online vehicle trajectory prediction using policy anticipation network and optimization based context reasoning"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "understanding human behaviors in crowds by imitating the decision making process"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "probabilistic prediction of vehicle semantic intention and motion"
    - "context aware trajectory prediction"
    - "desire distant future prediction in dynamic scenes with interacting agents"

Connected through hyperedge HE-3 (induced by source paper "reinforcing classical planning for adversary driving scenarios"):
  with members:
    - "deeptraffic crowdsourced hyperparameter tuning of deep reinforcement learning systems for multi agent dense traffic navigation"
    - "self supervised deep reinforcement learning with generalized computation graphs for robot navigation"
    - "carla an open urban driving simulator"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "socially aware motion planning with deep reinforcement learning"
    - "end to end learning for self driving cars"
    - "the option critic architecture"
    - "safe multi agent reinforcement learning for autonomous driving"

Connected through hyperedge HE-4 (induced by source paper "single step options for adversary driving"):
  with members:
    - "deeptraffic crowdsourced hyperparameter tuning of deep reinforcement learning systems for multi agent dense traffic navigation"
    - "self supervised deep reinforcement learning with generalized computation graphs for robot navigation"
    - "carla an open urban driving simulator"
    - "socially aware motion planning with deep reinforcement learning"
    - "end to end learning for self driving cars"
    - "the option critic architecture"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "giraffe using deep reinforcement learning to play chess"

Connected through hyperedge HE-5 (induced by source paper "recurrent flow guided semantic forecasting"):
  with members:
    - "nonlinear 3d face morphable model"
    - "dual motion gan for future flow embedded video prediction"
    - "predicting deeper into the future of semantic segmentation"
    - "decomposing motion and content for natural video sequence prediction"
    - "learning to generate long term future via hierarchical prediction"
    - "flow guided feature aggregation for video object detection"
    - "unsupervised learning of disentangled representations from video"
    - "pose invariant face alignment with a single cnn"

Connected through hyperedge HE-6 (induced by source paper "structured control nets for deep reinforcement learning"):
  with members:
    - "deep reinforcement learning that matters"
    - "parameter space noise for exploration"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "improving exploration in evolution strategies for deep reinforcement learning via a population of novelty seeking agents"
    - "least squares temporal difference learning for the linear quadratic regulator"
    - "towards generalization and simplicity in continuous control"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-7 (induced by source paper "future segmentation using 3d structure"):
  with members:
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "predicting deeper into the future of semantic segmentation"
    - "decomposing motion and content for natural video sequence prediction"
    - "unsupervised learning of depth and ego motion from video"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "se3 nets learning rigid body motion using deep neural networks"
    - "video scene parsing with predictive feature learning"
    - "on the sample complexity of end to end training vs semantic abstraction training"

Connected through hyperedge HE-8 (induced by source paper "deeptraffic crowdsourced hyperparameter tuning of deep reinforcement learning systems for multi agent dense traffic navigation"):
  with members:
    - "on improving deep reinforcement learning for pomdps"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "continuous control with deep reinforcement learning"
    - "deep reinforcement learning with double q learning"
    - "imagenet large scale visual recognition challenge"
    - "the arcade learning environment an evaluation platform for general agents"
    - "playing atari with deep reinforcement learning"

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)