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 "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.

Includes members:
  Member "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a supervised learning algorithm for multilayer spiking neural networks based on temporal coding toward energy efficient vlsi processor design")
  Member "computing with networks of nonlinear mechanical oscillators":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "physics for neuromorphic computing")
  Member "computability and complexity of unconventional computing devices":
    also connected through other hyperedges:
      (none)
  Member "mixed integer quadratic programming is in np":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep hypernetwork based mimo detection")
  Member "dna reservoir computing a novel molecular computing approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "recent advances in physical reservoir computing a review")

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.CC(Computational Complexity)