human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "compositional generalization through meta sequence to sequence learning"):
  Abstract: People can learn a new concept and use it compositionally, understanding how to "blicket twice" after learning how to "blicket." In contrast, powerful sequence-to-sequence (seq2seq) neural networks fail such tests of compositionality, especially when composing new concepts together with existing concepts. In this paper, I show how memory-augmented neural networks can be trained to generalize compositionally through meta seq2seq learning. In this approach, models train on a series of seq2seq problems to acquire the compositional skills needed to solve new seq2seq problems. Meta se2seq learning solves several of the SCAN tests for compositional learning and can learn to apply implicit rules to variables.

Includes members:
  Member "learning to infer program sketches":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "structural language models of code")
  Member "the omniglot challenge a 3 year progress report":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "torchmeta a meta learning library for pytorch")
  Member "human few shot learning of compositional instructions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning compositional rules via neural program synthesis")
  Member "good enough compositional data augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a benchmark for systematic generalization in grounded language understanding")
  Member "compositional generalization in a deep seq2seq model by separating syntax and semantics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a benchmark for systematic generalization in grounded language understanding")
  Member "mutual exclusivity as a challenge for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to learn words from visual scenes")
  Member "systematic generalization what is required and can it be learned":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "language as a cognitive tool to imagine goals in curiosity driven exploration")
  Member "jump to better conclusions scan both left and right":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "linguistic generalization and compositionality in modern artificial neural networks")

Question: Please tell me which class the source paper 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.CL(Computation and Language)