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: "computing optimal discrete readout weights in reservoir computing is np hard"
  Abstract: Abstract   We show NP-hardness of a generalized quadratic programming problem, which we called  unconstrained n-ary quadratic programming  (UNQP). This problem has recently become practically relevant in the context of novel memristor-based neuromorphic microchip designs, where solving the UNQP is a key operation for on-chip training of the neural network implemented on the chip. UNQP is the problem of finding a vector  v  ∈  S N   which minimizes      v  T     Q    v  +   v  T   c  ,     where     S  =  {   s  1   ,  …  ,   s  n   }  ⊂  Z     is a given set of eligible parameters for  v ,     Q  ∈   Z   N  ×  N       is positive semi-definite, and     c  ∈   Z  N     . In memristor-based neuromorphic hardware,  S  is physically given by a finite (and small) number of possible memristor states. The proof of NP-hardness is by reduction from the  unconstrained binary quadratic programming problem , which is a special case of UNQP where     S  =  {  0  ,  1  }     and which is known to be NP-hard.

Connected through hyperedge HE-1 (induced by source paper "neuromorphic electronic systems for reservoir computing"):
  with members:
    - "recent advances in physical reservoir computing a review"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "an online structural plasticity rule for generating better reservoirs"
    - "memory and information processing in neuromorphic systems"
    - "liquid state machine with dendritically enhanced readout for low power neuromorphic vlsi implementations"

Connected through hyperedge HE-2 (induced by source paper "network structure effects in reservoir computers"):
  with members:
    - "rapid time series prediction with a hardware based reservoir computer"
    - "using a reservoir computer to learn chaotic attractors with applications to chaos synchronization and cryptography"
    - "difference between memory and prediction in linear recurrent 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.CC(Computational Complexity)