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: "universal memcomputing machines"
  Abstract: We introduce the notion of  universal memcomputing machines  (UMMs): a class of brain-inspired general-purpose computing machines based on systems with memory, whereby processing and storing of information occur on the same physical location. We analytically prove that the memory properties of UMMs endow them with universal computing power (they are Turing-complete),  intrinsic parallelism, functional polymorphism, and information overhead , namely, their collective states can support exponential data compression directly in memory. We also demonstrate that a UMM has the same computational power as a nondeterministic Turing machine, namely, it can solve nondeterministic polynomial (NP)-complete problems in polynomial time. However, by virtue of its information overhead, a UMM needs only an amount of memory cells (memprocessors) that grows polynomially with the problem size. As an example, we provide the polynomial-time solution of the subset-sum problem and a simple hardware implementation of the same. Even though these results do not prove the statement NP = P within the Turing paradigm, the practical realization of these UMMs would represent a paradigm shift from the present von Neumann architectures, bringing us closer to brain-like neural computation.

Connected through hyperedge HE-1 (induced by source paper "a scalable photonic computer solving the subset sum problem"):
  with members:
    - "memcomputing np complete problems in polynomial time using polynomial resources and collective states"
    - "a faster pseudopolynomial time algorithm for subset sum"
    - "the parallel approach"
    - "solving the subset sum problem with a light based device"

Connected through hyperedge HE-2 (induced by source paper "aircraft loading optimization memcomputing the 5th airbus problem"):
  with members:
    - "a quantum inspired classical algorithm for recommendation systems"
    - "accelerating deep learning with memcomputing"
    - "stress testing memcomputing on hard combinatorial optimization problems"
    - "instantons in self organizing logic gates"
    - "memcomputing integer linear programming"
    - "perspective memcomputing leveraging memory and physics to compute efficiently"
    - "polynomial time solution of prime factorization and np complete problems with digital memcomputing machines"
    - "memcomputing np complete problems in polynomial time using polynomial resources and collective states"

Connected through hyperedge HE-3 (induced by source paper "asymptotic behavior of memristive circuits"):
  with members:
    - "a tutorial on formulating and using qubo models"
    - "memristors for the curious outsiders"
    - "memory and information processing in neuromorphic systems"
    - "memcomputing np complete problems in polynomial time using polynomial resources and collective states"
    - "self organization and solution of shortest path optimization problems with memristive networks"

Connected through hyperedge HE-4 (induced by source paper "digital memcomputing from logic to dynamics to topology"):
  with members:
    - "perspective memcomputing leveraging memory and physics to compute efficiently"
    - "polynomial time solution of prime factorization and np complete problems with digital memcomputing machines"

Connected through hyperedge HE-5 (induced by source paper "memristors for the curious outsiders"):
  with members:
    - "a neuromorphic systems approach to in memory computing with non ideal memristive devices from mitigation to exploitation"
    - "hierarchical temporal memory using memristor networks a survey"
    - "thermodynamic ram technology stack"
    - "perspective memcomputing leveraging memory and physics to compute efficiently"
    - "the case for rejecting the memristor as a fundamental circuit element"
    - "vertex coloring of graphs via phase dynamics of coupled oscillatory networks"
    - "polynomial time solution of prime factorization and np complete problems with digital memcomputing machines"
    - "temporal correlation detection using computational phase change memory"

Connected through hyperedge HE-6 (induced by source paper "accelerating deep learning with memcomputing"):
  with members:
    - "instantons in self organizing logic gates"
    - "weighted contrastive divergence"
    - "perspective memcomputing leveraging memory and physics to compute efficiently"
    - "memcomputing numerical inversion with self organizing logic gates"
    - "evidence of an exponential speed up in the solution of hard optimization problems"
    - "polynomial time solution of prime factorization and np complete problems with digital memcomputing machines"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "perspective memcomputing leveraging memory and physics to compute efficiently"):
  with members:
    - "instantons in self organizing logic gates"
    - "polynomial time solution of prime factorization and np complete problems with digital memcomputing machines"
    - "memcomputing np complete problems in polynomial time using polynomial resources and collective states"
    - "memcomputing with membrane memcapacitive systems"
    - "dynamic computing random access memory"
    - "the parallel approach"
    - "self organization and solution of shortest path optimization problems with memristive networks"

Connected through hyperedge HE-8 (induced by source paper "in memory computing on a photonic platform"):
  with members:
    - "mixed precision in memory computing"
    - "superconducting optoelectronic circuits for neuromorphic computing"
    - "temporal correlation detection using computational phase change memory"
    - "mixed precision memcomputing"
    - "buddy ram improving the performance and efficiency of bulk bitwise operations using dram"
    - "the parallel approach"

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)