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 "integrating temporal information to spatial information in a neural circuit"):
  Abstract: In this paper, we consider a network of spiking neurons with a deterministic synchronous firing rule at discrete time. We propose three problems -- "first consecutive spikes counting", "total spikes counting" and "$k$-spikes temporal to spatial encoding" -- to model how brains extract temporal information into spatial information from different neural codings. For a max input length $T$, we design three networks that solve these three problems with matching lower bounds in both time $O(T)$ and number of neurons $O(\log T)$ in all three questions.

Includes members:
  Member "counting to ten with two fingers compressed counting with spiking neurons":
    also connected through other hyperedges:
      (none)
  Member "a basic compositional model for spiking neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning hierarchically structured concepts")
  Member "on the algorithmic power of spiking neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exploring deep spiking neural networks for automated driving applications")
  Member "neuro ram unit with applications to similarity testing and compression in spiking neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "winner take all computation in spiking neural networks")
  Member "sparse coding by spiking neural networks convergence theory and computational results":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a spiking neural network with local learning rules derived from nonnegative similarity matching")
  Member "computational tradeoffs in biological neural networks self stabilizing winner take all networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "winner take all computation in spiking neural networks")
  Member "a theoretical basis for efficient computations with noisy spiking neurons":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the algorithmic power of spiking 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.DC(Distributed, Parallel, and Cluster Computing)