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: "perspective memcomputing leveraging memory and physics to compute efficiently"
  Abstract: It is well known that physical phenomena may be of great help in computing some difficult problems efficiently. A typical example is prime factorization that may be solved in polynomial time by exploiting quantum entanglement on a quantum computer. There are, however, other types of (non-quantum) physical properties that one may leverage to compute efficiently a wide range of hard problems. In this perspective, we discuss how to employ one such property, memory (time non-locality), in a novel physics-based approach to computation: Memcomputing. In particular, we focus on digital memcomputing machines (DMMs) that are scalable. DMMs can be realized with non-linear dynamical systems with memory. The latter property allows the realization of a new type of Boolean logic, one that is self-organizing. Self-organizing logic gates are “terminal-agnostic,” namely, they do not distinguish between the input and output terminals. When appropriately assembled to represent a given combinatorial/optimization problem, the corresponding self-organizing circuit converges to the equilibrium points that express the solutions of the problem at hand. In doing so, DMMs take advantage of the long-range order that develops during the transient dynamics. This collective dynamical behavior, reminiscent of a phase transition, or even the “edge of chaos,” is mediated by families of classical trajectories (instantons) that connect critical points of increasing stability in the system's phase space. The topological character of the solution search renders DMMs robust against noise and structural disorder. Since DMMs are non-quantum systems described by ordinary differential equations, not only can they be built in hardware with the available technology, they can also be simulated efficiently on modern classical computers. As an example, we will show the polynomial-time solution of the subset-sum problem for the worst cases, and point to other types of hard problems where simulations of DMMs' equations of motion on classical computers have already demonstrated substantial advantages over traditional approaches. We conclude this article by outlining further directions of study.It is well known that physical phenomena may be of great help in computing some difficult problems efficiently. A typical example is prime factorization that may be solved in polynomial time by exploiting quantum entanglement on a quantum computer. There are, however, other types of (non-quantum) physical properties that one may leverage to compute efficiently a wide range of hard problems. In this perspective, we discuss how to employ one such property, memory (time non-locality), in a novel physics-based approach to computation: Memcomputing. In particular, we focus on digital memcomputing machines (DMMs) that are scalable. DMMs can be realized with non-linear dynamical systems with memory. The latter property allows the realization of a new type of Boolean logic, one that is self-organizing. Self-organizing logic gates are “terminal-agnostic,” namely, they do not distinguish between the input and output terminals. When appropriately assembled to represent a given combinatorial/optimization problem, the...

Connected through hyperedge HE-1 (induced by source paper "p bits for probabilistic spin logic"):
  with members:
    - "weighted p bits for fpga implementation of probabilistic circuits"
    - "low energy deep belief networks using intrinsic sigmoidal spintronic based probabilistic neurons"
    - "voltage driven building block for hardware belief networks"
    - "stochastic spin orbit torque devices as elements for bayesian inference"
    - "stochastic spiking neural networks enabled by magnetic tunnel junctions from nontelegraphic to telegraphic switching regimes"
    - "hardware emulation of stochastic p bits for invertible logic"
    - "vlsi implementation of deep neural network using integral stochastic computing"
    - "polynomial time solution of prime factorization and np complete problems with digital memcomputing machines"

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"
    - "polynomial time solution of prime factorization and np complete problems with digital memcomputing machines"
    - "universal memcomputing machines"
    - "memcomputing np complete problems in polynomial time using polynomial resources and collective states"

Connected through hyperedge HE-3 (induced by source paper "digital memcomputing from logic to dynamics to topology"):
  with members:
    - "polynomial time solution of prime factorization and np complete problems with digital memcomputing machines"
    - "universal memcomputing machines"

Connected through hyperedge HE-4 (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"
    - "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"
    - "optimal dictionary for least squares representation"

Connected through hyperedge HE-5 (induced by source paper "accelerating deep learning with memcomputing"):
  with members:
    - "instantons in self organizing logic gates"
    - "weighted contrastive divergence"
    - "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"
    - "universal memcomputing machines"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "stress testing memcomputing on hard combinatorial optimization problems"):
  with members:
    - "accelerating deep learning with memcomputing"
    - "on the universality of memcomputing machines"
    - "evidence of an exponential speed up in the solution of hard optimization problems"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "memcomputing np complete problems in polynomial time using polynomial resources and collective states"
    - "the parallel approach"

Connected through hyperedge HE-7 (induced by source paper "on the universality of memcomputing machines"):
  with members:
    - "accelerating deep learning with memcomputing"
    - "memcomputing integer linear programming"
    - "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"
    - "universal memcomputing machines"
    - "memcomputing np complete problems in polynomial time using polynomial resources and collective states"
    - "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.ET(Emerging Technologies)