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: "automatically composing representation transformations as a means for generalization"
  Abstract: How can we build a learner that can capture the essence of what makes a hard problem more complex than a simple one, break the hard problem along characteristic lines into smaller problems it knows how to solve, and sequentially solve the smaller problems until the larger one is solved? To work towards this goal, we focus on learning to generalize in a particular family of problems that exhibit compositional and recursive structure: their solutions can be found by composing in sequence a set of reusable partial solutions. Our key idea is to recast the problem of generalization as a problem of learning algorithmic procedures: we can formulate a solution to this family as a sequential decision-making process over transformations between representations. Our formulation enables the learner to learn the structure and parameters of its own computation graph with sparse supervision, make analogies between problems by transforming one problem representation to another, and exploit modularity and reuse to scale to problems of varying complexity. Experiments on solving a variety of multilingual arithmetic problems demonstrate that our method discovers the hierarchical decomposition of a problem into its subproblems, generalizes out of distribution to unseen problem classes, and extrapolates to harder versions of the same problem, yielding a 10-fold reduction in sample complexity compared to a monolithic recurrent neural network.

Connected through hyperedge HE-1 (induced by source paper "towards modular algorithm induction"):
  with members:
    - "modular networks learning to decompose neural computation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "leveraging grammar and reinforcement learning for neural program synthesis"
    - "robustfill neural program learning under noisy i o"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "deepcoder learning to write programs"
    - "neural random access machines"
    - "learning simple algorithms from examples"

Connected through hyperedge HE-2 (induced by source paper "what can neural networks reason about"):
  with members:
    - "harnessing the power of infinitely wide deep nets on small data tasks"
    - "learning to make analogies by contrasting abstract relational structure"
    - "raven a dataset for relational and analogical visual reasoning"
    - "analysing mathematical reasoning abilities of neural models"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "on the turing completeness of modern neural network architectures"

Connected through hyperedge HE-3 (induced by source paper "routing networks and the challenges of modular and compositional computation"):
  with members:
    - "a meta mdp approach to exploration for lifelong reinforcement learning"
    - "reinforcement learning without backpropagation or a clock"
    - "the termination critic"
    - "modular networks learning to decompose neural computation"
    - "learning abstract options"
    - "evolutionary architecture search for deep multitask networks"
    - "modular meta learning"
    - "diversity is all you need learning skills without a reward function"

Connected through hyperedge HE-4 (induced by source paper "zero shot task adaptation by homoiconic meta mapping"):
  with members:
    - "lgm net learning to generate matching networks for few shot learning"
    - "online meta learning"
    - "learnable embedding space for efficient neural architecture compression"
    - "zero shot task transfer"
    - "task2vec task embedding for meta learning"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "meta learning with latent embedding optimization"
    - "universal successor features approximators"

Connected through hyperedge HE-5 (induced by source paper "using hindsight to anchor past knowledge in continual learning"):
  with members:
    - "dataset distillation"
    - "continual learning with tiny episodic memories"
    - "on first order meta learning algorithms"
    - "task free continual learning"
    - "reptile a scalable metalearning algorithm"
    - "modular meta learning"
    - "experience replay for continual learning"
    - "efficient lifelong learning with a gem"

Connected through hyperedge HE-6 (induced by source paper "cooperative learning of disjoint syntax and semantics"):
  with members:
    - "on tree based neural sentence modeling"
    - "listops a diagnostic dataset for latent tree learning"
    - "latent tree learning with differentiable parsers shift reduce parsing and chart parsing"
    - "towards dynamic computation graphs via sparse latent structure"
    - "deep contextualized word representations"
    - "differentiable perturb and parse semi supervised parsing with a structured variational autoencoder"
    - "learning to generate reviews and discovering sentiment"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"

Connected through hyperedge HE-7 (induced by source paper "measuring compositional generalization a comprehensive method on realistic data"):
  with members:
    - "measuring compositionality in representation learning"
    - "learning by abstraction the neural state machine"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "analysing mathematical reasoning abilities of neural models"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "universal transformers"
    - "compositional generalization in a deep seq2seq model by separating syntax and semantics"
    - "improving text to sql evaluation methodology"

Connected through hyperedge HE-8 (induced by source paper "compositional generalization for primitive substitutions"):
  with members:
    - "human few shot learning of compositional instructions"
    - "memorize or generalize searching for a compositional rnn in a haystack"
    - "efficient lifelong learning with a gem"
    - "jump to better conclusions scan both left and right"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "rearranging the familiar testing compositional generalization in recurrent networks"
    - "memory aware synapses learning what not to forget"
    - "attention is all you need"

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)