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: "fast task inference with variational intrinsic successor features"
  Abstract: It has been established that diverse behaviors spanning the controllable subspace of an Markov decision process can be trained by rewarding a policy for being distinguishable from other policies \citep{gregor2016variational, eysenbach2018diversity, warde2018unsupervised}. However, one limitation of this formulation is generalizing behaviors beyond the finite set being explicitly learned, as is needed for use on subsequent tasks. Successor features \citep{dayan93improving, barreto2017successor} provide an appealing solution to this generalization problem, but require defining the reward function as linear in some grounded feature space. In this paper, we show that these two techniques can be combined, and that each method solves the other's primary limitation. To do so we introduce Variational Intrinsic Successor FeatuRes (VISR), a novel algorithm which learns controllable features that can be leveraged to provide enhanced generalization and fast task inference through the successor feature framework. We empirically validate VISR on the full Atari suite, in a novel setup wherein the rewards are only exposed briefly after a long unsupervised phase. Achieving human-level performance on 14 games and beating all baselines, we believe VISR represents a step towards agents that rapidly learn from limited feedback.

Connected through hyperedge HE-1 (induced by source paper "seed rl scalable and efficient deep rl with accelerated central inference"):
  with members:
    - "exploiting hierarchy for learning and transfer in kl regularized rl"
    - "torchbeast a pytorch platform for distributed rl"
    - "options as responses grounding behavioural hierarchies in multi agent rl"
    - "discovery of useful questions as auxiliary tasks"
    - "google research football a novel reinforcement learning environment"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "cross view policy learning for street navigation"
    - "neural replicator dynamics"

Connected through hyperedge HE-2 (induced by source paper "mega reward achieving human level play without extrinsic rewards"):
  with members:
    - "diversity driven extensible hierarchical reinforcement learning"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "exploration by random network distillation"
    - "large scale study of curiosity driven learning"
    - "world models"
    - "diversity is all you need learning skills without a reward function"
    - "generalisation of structural knowledge in the hippocampal entorhinal system"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-3 (induced by source paper "disentangled cumulants help successor representations transfer to new tasks"):
  with members:
    - "transfer in deep reinforcement learning using successor features and generalised policy improvement"
    - "regularized hierarchical policies for compositional transfer in robotics"
    - "visual reinforcement learning with imagined goals"
    - "diversity is all you need learning skills without a reward function"
    - "curiosity driven exploration of learned disentangled goal spaces"
    - "towards a definition of disentangled representations"
    - "eigenoption discovery through the deep successor representation"
    - "deep variational canonical correlation analysis"

Connected through hyperedge HE-4 (induced by source paper "planning in hierarchical reinforcement learning guarantees for using local policies"):
  with members:
    - "hierarchical reinforcement learning approximating optimal discounted tsp using local policies"
    - "universal successor features approximators"
    - "feudal networks for hierarchical reinforcement learning"
    - "hybrid reward architecture for reinforcement learning"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "the option critic architecture"
    - "successor features for transfer in 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)