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: "the limits and potentials of deep learning for robotics"
  Abstract: The application of deep learning in robotics leads to very specific problems and research questions that are typically not addressed by the computer vision and machine learning communities. In this paper we discuss a number of robotics-specific learning, reasoning, and embodiment challenges for deep learning. We explain the need for better evaluation metrics, highlight the importance and unique challenges for deep robotic learning in simulation, and explore the spectrum between purely data-driven and model-driven approaches. We hope this paper provides a motivating overview of important research directions to overcome the current limitations, and help fulfill the promising potentials of deep learning in robotics.

Connected through hyperedge HE-1 (induced by source paper "on the convergence of artificial intelligence and distributed ledger technology a scoping review and future research agenda"):
  with members:
    - "ekiden a platform for confidentiality preserving trustworthy and performant smart contracts"
    - "energy recycling blockchain with proof of deep learning"
    - "decentralized and collaborative ai on blockchain"
    - "sok transparent dishonesty front running attacks on blockchain"
    - "reliable federated learning for mobile networks"
    - "keystone an open framework for architecting tees"
    - "tensorscone a secure tensorflow framework using intel sgx"
    - "coin ai a proof of useful work scheme for blockchain based distributed deep learning"

Connected through hyperedge HE-2 (induced by source paper "learning task driven control policies via information bottlenecks"):
  with members:
    - "differentiable algorithm networks for composable robot learning"
    - "learning efficient representation for intrinsic motivation"
    - "on variational bounds of mutual information"
    - "learning representations in reinforcement learning an information bottleneck approach"
    - "unsupervised visuomotor control through distributional planning networks"
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "particle filter networks with application to visual localization"
    - "unifying map and landmark based representations for visual navigation"

Connected through hyperedge HE-3 (induced by source paper "openloris object a robotic vision dataset and benchmark for lifelong deep learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "generative adversarial networks a survey and taxonomy"
    - "neural dynamics discovery via gaussian process recurrent neural networks"
    - "are we ready for service robots the openloris scene datasets for lifelong slam"
    - "neuroscore a brain inspired evaluation metric for generative adversarial networks"
    - "incremental classifier learning with generative adversarial networks"
    - "don t forget there is more than forgetting new metrics for continual learning"
    - "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"

Connected through hyperedge HE-4 (induced by source paper "reinforcement learning for pomdp partitioned rollout and policy iteration with application to autonomous sequential repair problems"):
  with members:
    - "active rendezvous for multi robot pose graph optimization using sensing over wi fi"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "decentralized control of partially observable markov decision processes using belief space macro actions"
    - "solving pomdps by searching in policy space"
    - "solving pomdps by searching the space of finite policies"
    - "a function approximation approach to estimation of policy gradient for pomdp with structured policies"
    - "discretized approximations for pomdp with average cost"

Connected through hyperedge HE-5 (induced by source paper "smt based robot transition repair"):
  with members:
    - "robustfill neural program learning under noisy i o"
    - "human in the loop slam"
    - "a real time solver for time optimal control of omnidirectional robots with bounded acceleration"
    - "tortoise interactive system configuration repair"
    - "proximal policy optimization algorithms"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "reinforcement learning with parameterized actions"
    - "deep reinforcement learning in parameterized action space"

Connected through hyperedge HE-6 (induced by source paper "what can robotics research learn from computer vision research"):
  with members:
    - "a mask rcnn baseline for probabilistic object detection"
    - "do imagenet classifiers generalize to imagenet"
    - "the fishyscapes benchmark measuring blind spots in semantic segmentation"
    - "yolov3 an incremental improvement"
    - "the robotarium a remotely accessible swarm robotics research testbed"
    - "towards open world recognition"
    - "orb slam a versatile and accurate monocular slam system"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-7 (induced by source paper "anomaly detection in video data based on probabilistic latent space models"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "hierarchy of gans for learning embodied self awareness model"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "action recognition from static datasets to moving robots"
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"
    - "a study and comparison of human and deep learning recognition performance under visual distortions"
    - "training adversarial discriminators for cross channel abnormal event detection in crowds"

Connected through hyperedge HE-8 (induced by source paper "a survey of knowledge representation in service robotics"):
  with members:
    - "long activity video understanding using functional object oriented network"
    - "identifying object states in cooking related images"
    - "functional object oriented network construction expansion"
    - "blitznet a real time deep network for scene understanding"
    - "physical problem solving joint planning with symbolic geometric and dynamic constraints"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "affordancenet an end to end deep learning approach for object affordance detection"
    - "dex net 3 0 computing robust robot suction grasp targets in point clouds using a new analytic model and deep 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.RO(Robotics)