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 "measuring abstract reasoning in neural networks"):
  Abstract: Whether neural networks can learn abstract reasoning or whether they merely rely on superficial statistics is a topic of recent debate. Here, we propose a dataset and challenge designed to probe abstract reasoning, inspired by a well-known human IQ test. To succeed at this challenge, models must cope with various generalisation `regimes' in which the training and test data differ in clearly-defined ways. We show that popular models such as ResNets perform poorly, even when the training and test sets differ only minimally, and we present a novel architecture, with a structure designed to encourage reasoning, that does significantly better. When we vary the way in which the test questions and training data differ, we find that our model is notably proficient at certain forms of generalisation, but notably weak at others. We further show that the model's ability to generalise improves markedly if it is trained to predict symbolic explanations for its answers. Altogether, we introduce and explore ways to both measure and induce stronger abstract reasoning in neural networks. Our freely-available dataset should motivate further progress in this direction.

Includes members:
  Member "deep learning a critical appraisal":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence")
  Member "an empirical evaluation of visual question answering for novel objects":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unshuffling data for improved generalization")
  Member "building machines that learn and think for themselves commentary on lake et al behavioral and brain sciences 2017":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "alternative techniques for mapping paths to hlai")
  Member "iq of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "evaluating the progress of deep learning for visual relational concepts")
  Member "discovering objects and their relations from entangled scene representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural consciousness flow")
  Member "measuring the tendency of cnns to learn surface statistical regularities":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an explicit local and global representation disentanglement framework with applications in deep clustering and unsupervised object detection")
  Member "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks")
  Member "program induction by rationale generation learning to solve and explain algebraic word problems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs")

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.LG(Machine Learning)