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: "feed forward neural networks need inductive bias to learn equality relations"
  Abstract: Basic binary relations such as equality and inequality are fundamental to relational data structures. Neural networks should learn such relations and generalise to new unseen data. We show in this study, however, that this generalisation fails with standard feed-forward networks on binary vectors. Even when trained with maximal training data, standard networks do not reliably detect equality.We introduce differential rectifier (DR) units that we add to the network in different configurations. The DR units create an inductive bias in the networks, so that they do learn to generalise, even from small numbers of examples and we have not found any negative effect of their inclusion in the network. Given the fundamental nature of these relations, we hypothesize that feed-forward neural network learning benefits from inductive bias in other relations as well. Consequently, the further development of suitable inductive biases will be beneficial to many tasks in relational learning with neural networks.

Connected through hyperedge HE-1 (induced by source paper "learning algorithmic solutions to symbolic planning tasks with a neural computer"):
  with members:
    - "unsupervised predictive memory in a goal directed agent"
    - "improving exploration in evolution strategies for deep reinforcement learning via a population of novelty seeking agents"
    - "neural programmer inducing latent programs with gradient descent"
    - "learning simple algorithms from examples"
    - "inferring algorithmic patterns with stack augmented recurrent nets"
    - "neural gpus learn algorithms"
    - "ask me anything dynamic memory networks for natural language processing"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "factors for the generalisation of identity relations by neural networks"):
  with members:
    - "extrapolation in nlp"
    - "neural arithmetic logic units"
    - "relational inductive bias for physical construction in humans and machines"
    - "prototypical networks for few shot learning"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

Connected through hyperedge HE-3 (induced by source paper "weight priors for learning identity relations"):
  with members:
    - "memorize or generalize searching for a compositional rnn in a haystack"
    - "deep learning a critical appraisal"
    - "modelling identity rules with neural networks"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "adam a method for stochastic optimization"

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)