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: "hardware conditioned policies for multi robot transfer learning"
  Abstract: Deep reinforcement learning could be used to learn dexterous robotic policies but it is challenging to transfer them to new robots with vastly different hardware properties. It is also prohibitively expensive to learn a new policy from scratch for each robot hardware due to the high sample complexity of modern state-of-the-art algorithms. We propose a novel approach called \textit{Hardware Conditioned Policies} where we train a universal policy conditioned on a vector representation of robot hardware. We considered robots in simulation with varied dynamics, kinematic structure, kinematic lengths and degrees-of-freedom. First, we use the kinematic structure directly as the hardware encoding and show great zero-shot transfer to completely novel robots not seen during training. For robots with lower zero-shot success rate, we also demonstrate that fine-tuning the policy network is significantly more sample-efficient than training a model from scratch. In tasks where knowing the agent dynamics is important for success, we learn an embedding for robot hardware and show that policies conditioned on the encoding of hardware tend to generalize and transfer well. The code and videos are available on the project webpage: this https URL.

Connected through hyperedge HE-1 (induced by source paper "hierarchically decoupled imitation for morphological transfer"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "dynamics aware unsupervised discovery of skills"
    - "mcp learning composable hierarchical control with multiplicative compositional policies"
    - "hierarchical reinforcement learning with advantage based auxiliary rewards"
    - "learning to control self assembling morphologies a study of generalization via modularity"
    - "skill transfer in deep reinforcement learning under morphological heterogeneity"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "soft actor critic algorithms and applications"

Connected through hyperedge HE-2 (induced by source paper "skill transfer in deep reinforcement learning under morphological heterogeneity"):
  with members:
    - "exploiting hierarchy for learning and transfer in kl regularized rl"
    - "synthesized policies for transfer and adaptation across tasks and environments"
    - "learning discrete and continuous factors of data via alternating disentanglement"
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "policy transfer with strategy optimization"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "visual reinforcement learning with imagined goals"

Connected through hyperedge HE-3 (induced by source paper "sim to real transfer for biped locomotion"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "learning symmetric and low energy locomotion"
    - "learning dexterous in hand manipulation"
    - "policy transfer with strategy optimization"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "sim to real learning agile locomotion for quadruped robots"
    - "closing the sim to real loop adapting simulation randomization with real world experience"

Connected through hyperedge HE-4 (induced by source paper "learning to control self assembling morphologies a study of generalization via modularity"):
  with members:
    - "reinforcement learning for improving agent design"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "an integrated system for perception driven autonomy with modular robots"
    - "graph networks as learnable physics engines for inference and control"
    - "unity a general platform for intelligent agents"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "relational inductive biases deep learning and graph networks"
    - "glomo unsupervisedly learned relational graphs as transferable representations"

Connected through hyperedge HE-5 (induced by source paper "learning domain randomization distributions for training robust locomotion policies"):
  with members:
    - "active domain randomization"
    - "bayessim adaptive domain randomization via probabilistic inference for robotics simulators"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "learning dexterous in hand manipulation"
    - "policy transfer with strategy optimization"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "assessing generalization in 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.RO(Robotics)