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: "towards learning abstract representations for locomotion planning in high dimensional state spaces"
  Abstract: Ground robots which are able to navigate a variety of terrains are needed in many domains. One of the key aspects is the capability to adapt to the ground structure, which can be realized through movable body parts coming along with additional degrees of freedom (DoF). However, planning respective locomotion is challenging since suitable representations result in large state spaces. Employing an additional abstract representation---which is coarser, lower-dimensional, and semantically enriched---can support the planning. #R##N#While a desired robot representation and action set of such an abstract representation can be easily defined, the cost function requires large tuning efforts. We propose a method to represent the cost function as a CNN. Training of the network is done on generated artificial data, while it generalizes well to the abstraction of real world scenes. We further apply our method to the problem of search-based planning of hybrid driving-stepping locomotion. The abstract representation is used as a powerful informed heuristic which accelerates planning by multiple orders of magnitude.

Connected through hyperedge HE-1 (induced by source paper "mapping state space using landmarks for universal goal reaching"):
  with members:
    - "robot motion planning in learned latent spaces"
    - "hierarchical reinforcement learning with hindsight"
    - "composable planning with attributes"
    - "temporal difference models model free deep rl for model based control"
    - "learning latent dynamics for planning from pixels"
    - "semi parametric topological memory for navigation"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "deeply informed neural sampling for robot motion planning"

Connected through hyperedge HE-2 (induced by source paper "value iteration networks on multiple levels of abstraction"):
  with members:
    - "rgb d object detection and semantic segmentation for autonomous manipulation in clutter"
    - "planning hybrid driving stepping locomotion on multiple levels of abstraction"
    - "gated path planning networks"
    - "supervised autonomous locomotion and manipulation for disaster response with a centaur like robot"
    - "learning generalized reactive policies using deep neural networks"
    - "generalized value iteration networks life beyond lattices"
    - "anytime hybrid driving stepping locomotion planning"
    - "qmdp net deep learning for planning under partial observability"

Connected through hyperedge HE-3 (induced by source paper "deepgait planning and control of quadrupedal gaits using deep reinforcement learning"):
  with members:
    - "footstep planning for autonomous walking over rough terrain"
    - "learning agile and dynamic motor skills for legged robots"
    - "fast and continuous foothold adaptation for dynamic locomotion through cnns"
    - "learning to walk via deep reinforcement learning"
    - "emergence of locomotion behaviours in rich environments"
    - "proximal policy optimization algorithms"
    - "high dimensional continuous control using generalized advantage estimation"
    - "trust region policy optimization"

Connected through hyperedge HE-4 (induced by source paper "flexible disaster response of tomorrow final presentation and evaluation of the centauro system"):
  with members:
    - "transferring category based functional grasping skills by latent space non rigid registration"
    - "autonomous dual arm manipulation of familiar objects"
    - "transferring grasping skills to novel instances by latent space non rigid registration"
    - "anytime hybrid driving stepping locomotion planning"
    - "the cityscapes dataset for semantic urban scene understanding"

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)