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: "representational distance learning for deep neural networks"
  Abstract: Deep neural networks (DNNs) provide useful models of visual representational transformations. We present a method that enables a DNN (student) to learn from the internal representational spaces of a reference model (teacher), which could be another DNN or, in the future, a biological brain. Representational spaces of the student and the teacher are characterized by representational distance matrices (RDMs). We propose representational distance learning (RDL), a stochastic gradient descent method that drives the RDMs of the student to approximate the RDMs of the teacher. We demonstrate that RDL is competitive with other transfer learning techniques for two publicly available benchmark computer vision datasets (MNIST and CIFAR-100), while allowing for architectural differences between student and teacher. By pulling the student's RDMs towards those of the teacher, RDL significantly improved visual classification performance when compared to baseline networks that did not use transfer learning. In the future, RDL may enable combined supervised training of deep neural networks using task constraints (e.g. images and category labels) and constraints from brain-activity measurements, so as to build models that replicate the internal representational spaces of biological brains.

Connected through hyperedge HE-1 (induced by source paper "representation similarity analysis for efficient task taxonomy transfer learning"):
  with members:
    - "moments in time dataset one million videos for event understanding"
    - "taskonomy disentangling task transfer learning"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "blitznet a real time deep network for scene understanding"
    - "joint prediction of depths normals and surface curvature from rgb images using cnns"
    - "rethinking atrous convolution for semantic image segmentation"
    - "mask r cnn"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"

Connected through hyperedge HE-2 (induced by source paper "learning from brains how to regularize machines"):
  with members:
    - "similarity of neural network representations revisited"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "a neurobiological evaluation metric for neural network model search"
    - "towards the first adversarially robust neural network model on mnist"
    - "decoupling direction and norm for efficient gradient based l2 adversarial attacks and defenses"
    - "generalisation in humans and deep neural networks"
    - "sparsefool a few pixels make a big difference"

Connected through hyperedge HE-3 (induced by source paper "training neural networks to mimic the brain improves object recognition performance"):
  with members:
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"
    - "fitnets hints for thin deep nets"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "a neurobiological evaluation metric for neural network model search"):
  with members:
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "visual psychophysics for making face recognition algorithms more explainable"
    - "totally looks like how humans compare compared to machines"
    - "monas multi objective neural architecture search using reinforcement learning"
    - "psychlab a psychology laboratory for deep reinforcement learning agents"
    - "generalisation in humans and deep neural networks"
    - "teacher guided architecture search"
    - "efficient neural architecture search via parameter sharing"

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)