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: "tensor switching networks"
  Abstract: We present a novel neural network algorithm, the Tensor Switching (TS) network, which generalizes the Rectified Linear Unit (ReLU) nonlinearity to tensor-valued hidden units. The TS network copies its entire input vector to different locations in an expanded representation, with the location determined by its hidden unit activity. In this way, even a simple linear readout from the TS representation can implement a highly expressive deep-network-like function. The TS network hence avoids the vanishing gradient problem by construction, at the cost of larger representation size. We develop several methods to train the TS network, including equivalent kernels for infinitely wide and deep TS networks, a one-pass linear learning algorithm, and two backpropagation-inspired representation learning algorithms. Our experimental results demonstrate that the TS network is indeed more expressive and consistently learns faster than standard ReLU networks.

Connected through hyperedge HE-1 (induced by source paper "ray interference a source of plateaus in deep reinforcement learning"):
  with members:
    - "the value function polytope in reinforcement learning"
    - "multi task deep reinforcement learning with popart"
    - "learning dexterous in hand manipulation"
    - "rudder return decomposition for delayed rewards"
    - "automated curriculum learning for neural networks"
    - "zero shot task generalization with multi task deep reinforcement learning"
    - "gradient episodic memory for continual learning"
    - "pathnet evolution channels gradient descent in super neural networks"

Connected through hyperedge HE-2 (induced by source paper "federated multi task learning"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "cocoa a general framework for communication efficient distributed optimization"
    - "distributed multi task relationship learning"
    - "communication efficient learning of deep networks from decentralized data"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "asynchronous multi task learning"
    - "distributed multi task learning with shared representation"
    - "convexified convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "strongly typed agents are guaranteed to interact safely"):
  with members:
    - "deep generalized canonical correlation analysis"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "nips 2016 tutorial generative adversarial networks"
    - "concrete problems in ai safety"
    - "strongly typed recurrent neural networks"
    - "safe learning of regions of attraction for uncertain nonlinear systems with gaussian processes"
    - "learning deep control policies for autonomous aerial vehicles with mpc guided policy search"
    - "kickback cuts backprop s red tape biologically plausible credit assignment in neural networks"

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.NE(Neural and Evolutionary Computing)