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: "active sites model for the b matrix approach"
  Abstract: This paper continues on the work of the B-Matrix approach in hebbian learning proposed by Dr. Kak. It reports the results on methods of improving the memory retrieval capacity of the hebbian neural network which implements the B-Matrix approach. Previously, the approach to retrieving the memories from the network was to clamp all the individual neurons separately and verify the integrity of these memories. Here we present a network with the capability to identify the "active sites" in the network during the training phase and use these "active sites" to generate the memories retrieved from these neurons. Three methods are proposed for obtaining the update order of the network from the proximity matrix when multiple neurons are to be clamped. We then present a comparison between the new methods to the classical case and also among the methods themselves.

Connected through hyperedge HE-1 (induced by source paper "memory retrieval in the b matrix neural network"):
  with members:
    - "delta learning rule for the active sites model"
    - "memory retrieved from single neurons"
    - "single neuron memories and the network s proximity matrix"

Connected through hyperedge HE-2 (induced by source paper "using variable threshold to increase capacity in a feedback neural network"):
  with members:
    - "on the mesh array for matrix multiplication"
    - "unary coding for neural network learning"
    - "single neuron memories and the network s proximity matrix"

Connected through hyperedge HE-3 (induced by source paper "memory storage in a variable threshold neural network"):
  with members:
    - "on the mesh array for matrix multiplication"
    - "single neuron memories and the network s proximity matrix"

Connected through hyperedge HE-4 (induced by source paper "delta learning rule for the active sites model"):
  with members:
    - "single neuron memories and the network s proximity matrix"

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)