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: "beyond holistic object recognition enriching image understanding with part states"
  Abstract: Important high-level vision tasks such as human-object interaction, image captioning and robotic manipulation require rich semantic descriptions of objects at part level. Based upon previous work on part localization, in this paper, we address the problem of inferring rich semantics imparted by an object part in still images. We propose to tokenize the semantic space as a discrete set of part states. Our modeling of part state is spatially localized, therefore, we formulate the part state inference problem as a pixel-wise annotation problem. An iterative part-state inference neural network is specifically designed for this task, which is efficient in time and accurate in performance. Extensive experiments demonstrate that the proposed method can effectively predict the semantic states of parts and simultaneously correct localization errors, thus benefiting a few visual understanding applications. The other contribution of this paper is our part state dataset which contains rich part-level semantic annotations.

Connected through hyperedge HE-1 (induced by source paper "unbiased scene graph generation via rich and fair semantic extraction"):
  with members:
    - "knowledge embedded routing network for scene graph generation"
    - "graph r cnn for scene graph generation"
    - "attentive relational networks for mapping images to scene graphs"
    - "visual relationship detection based on guided proposals and semantic knowledge distillation"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "neural motifs scene graph parsing with global context"
    - "weakly supervised learning of visual relations"
    - "detecting visual relationships with deep relational networks"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised complementary parts models for fine grained image classification from the bottom up"):
  with members:
    - "large scale fine grained categorization and domain specific transfer learning"
    - "multi attention multi class constraint for fine grained image recognition"
    - "features for multi target multi camera tracking and re identification"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "hierarchical bilinear pooling for fine grained visual recognition"
    - "explicit inductive bias for transfer learning with convolutional networks"
    - "network dissection quantifying interpretability of deep visual representations"

Connected through hyperedge HE-3 (induced by source paper "hake human activity knowledge engine"):
  with members:
    - "ican instance centric attention network for human object interaction detection"
    - "pairwise body part attention for recognizing human object interactions"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "transferable interactiveness knowledge for human object interaction detection"
    - "learning to detect human object interactions"
    - "care about you towards large scale human centric visual relationship detection"
    - "attentional pooling for action recognition"

Connected through hyperedge HE-4 (induced by source paper "transferable interactiveness knowledge for human object interaction detection"):
  with members:
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "pose flow efficient online pose tracking"
    - "ican instance centric attention network for human object interaction detection"
    - "srda generating instance segmentation annotation via scanning reasoning and domain adaptation"
    - "graph r cnn for scene graph generation"
    - "crowdpose efficient crowded scenes pose estimation and a new benchmark"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "pairwise body part attention for recognizing human object interactions"

Connected through hyperedge HE-5 (induced by source paper "pixels to graphs by associative embedding"):
  with members:
    - "vip cnn visual phrase guided convolutional neural network"
    - "towards context aware interaction recognition"
    - "detecting visual relationships with deep relational networks"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "scene graph generation by iterative message passing"
    - "learning to detect human object interactions"
    - "on support relations and semantic scene graphs"
    - "discovering objects and their relations from entangled scene representations"

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.CV(Computer Vision and Pattern Recognition)